Analysis of thermal discomfort associated with synoptic conditions in the city of Pelotas, southernmost region of Brazil.

Here, we evaluated the influence of outdoor environmental conditions (synoptic weather conditions) on human thermal discomfort in the five macro-regions of Pelotas city, located in the southernmost region of Brazil. To do this, meteorological sensors (HOBO MX2301A) were installed outside the residences to measure the air temperature, dew point temperature, and relative humidity between 18 January and 20 August 2019. Two well-established simplified biometeorological indices were examined seasonally: (i) humidex for the summer months and (ii) effective temperature as a function of wind for the autumn and winter months. Our findings showed seasonal differences related to human thermal discomfort and outdoor environmental conditions. The thermal discomfort was highest in the afternoons during the summer months and at night during the winter months. The seasonal variation in human thermal discomfort was highly associated with the meteorological conditions. In summer, the presence of the South Atlantic Subtropical Anticyclone (SASA) contributed to heat stress. The SASA combined with the continent's low humidity contributed to the perceived sensation of thermal discomfort. In the winter, thermal discomfort was associated with the decrease in air humidity caused by high atmospheric pressure systems, which led to a decrease in both air temperature and air moisture content. Our findings suggest that a better understanding of the complex interplay between outdoor environmental factors and human thermal comfort is needed in order to mitigate the negative effects of thermal discomfort.

Relationship between meteorological variables and pneumonia in children in the Metropolitan Region of Porto Alegre, Brazil.

This work aims to analyze the relationship between meteorological conditions and the occurrence of hospital admissions for pneumonia in children under 5 years of age in the Metropolitan Region of Porto Alegre, Brazil, from 1998 to 2017. To this end, data from hospital admissions obtained from the Unified Health System database (DATASUS) were used and classified into two groups: acute respiratory infections (ARI) and asthma, according to the international classification of diseases, tenth edition (ICD-10). Data regarding meteorological variables were also used: temperature, relative humidity, atmospheric pressure and wind speed, at 12Z and 18Z, as well as the Thermal Comfort Index (TCI), Effective Temperature as a function of the wind (ETw) and Windchill (W). From the data obtained, a descriptive analysis of the diseases and a statistical analysis with the analysis of correlation and main components were performed. Results showed that pneumonia (catalogued in the ICD-10 as J12 to J18) was the main cause of hospitalizations in children. The annual, monthly and daily hospitalization frequency distributions showed higher rates of admissions occurring in the months of May to September. The peaks of admissions and high admissions (HA) occurred mainly in the winter months (June, July and August), and in 1998. Meanwhile, the correlation and principal component analysis showed an increase in hospital admissions due to pneumonia related to a decrease in temperature and ETw and W indices (negative anomalies) and an increase in atmospheric pressure and relative humidity (positive anomalies).

Coupled models using radar network database to assess vehicular emissions in current and future scenarios.

Vehicles are one of the most significant sources of air pollutant emissions in urban areas, and their real contribution always needs to be updated to predict impacts on air quality. Radar databases and traffic counts using statistical modeling is an alternative and low-cost approach to produce traffic activities data in each urban street to be used as input to predict vehicular emissions. In this work, we carried out a spatial statistical analysis of local radar data and calculated traffic flow using local radar data combined with different statistical models. Future scenarios about vehicle emission inventory to define public policies were also proposed and analyzed for Belo Horizonte (BH), a Brazilian State capital, with the third-largest metropolitan region in the country. The Normal-Neighborhood Model (i.e., the mixed effect model with random effect in the neighborhood, radar type, and in the regional area) was used to calculate traffic flow in each urban street. Results showed average reductions in CO (4.5%), NMHC (3.0%), NOx (3.0%) and PM2.5 (6.2%) emissions even with an increase in fleet composition (25% in average). The decrease is a result of the implementation of emission control programs by the government, improvements vehicles technologies, and the quality of fuels. Prediction of traffic data from radar databases has proven to be useful for avoiding the high costs of performing origin-destination surveys and traffic modeling using commercial software. Radar databases can provide many potential benefits for research and analysis in environmental and transportation planning. These findings can be incorporated in future investigations to implement public policies on vehicular emission reduction in urban areas and to advance environmental health effects research and human health risk assessment.

Analysis of indoor human thermal comfort in Pelotas municipality, extreme southern Brazil.

The indoor human thermal comfort (HTC) was investigated in residences located in the Pelotas City, southern Brazil, by the effective temperature index (ETI). In this study, temperature and relative humidity were measured inside 429 houses, located in different regions of Pelotas city, from January 11 to August 27, 2019. Samples were obtained using HOBO data loggers, indoor sensors, installed in different regions of the municipality, in the context of a cohort study of children between 2 and 4 years old and their respective mothers, led by Epidemiological Research Center of the Federal University of Pelotas (UFPEL). In general, all regions had average hourly values of effective temperature index above the comfort zone in summer and below the comfort zone in the winter. In terms of spatial variability, the indoor HTC was dependent on environmental factors such as lake breeze and indoor behavior factors, such as the use of air conditioning system in the downtown buildings.

Top-down vehicle emission inventory for spatial distribution and dispersion modeling of particulate matter.

Emission inventories are one of the most critical inputs for the successful modeling of air quality. The performance of the modeling results is directly affected by the quality of atmospheric emission inventories. Consequently, the development of representative inventories is always required. Due to the lack of regional inventories in Brazil, this study aimed to investigate the use of the particulate matter (PM) emission estimation from the Brazilian top-down vehicle emission inventory (VEI) of 2012 for air quality modeling. Here, we focus on road vehicles since they are usually responsible for significant emissions of PM in urban areas. The total Brazilian emission of PM (63,000 t year-1) from vehicular sources was distributed into the urban areas of 5557 municipalities, with 1-km2 grid spacing, considering two approaches: (i) population and (ii) fleet of each city. A comparison with some local inventories is discussed. The inventory was compiled in the PREP-CHEM-SRC processor tool. One-month modeling (August 2015) was performed with WRF-Chem for the four metropolitan areas of Brazilian Southeast: Belo Horizonte (MABH), Great Vitória (MAGV), Rio de Janeiro (MARJ), and São Paulo (MASP). In addition, modeling with the Emission Database for Global Atmospheric Research (EDGAR) inventory was carried out to compare the results. Overall, EDGAR inventory obtained higher PM emissions than the VEI segregated by population and fleet, which is expected owing to considerations of additional sources of emission (e.g., industrial and residential). This higher emission of EDGAR resulted in higher PM10 and PM2.5 concentrations, overestimating the observations in MASP, while the proposed inventory well represented the ambient concentrations, obtaining better statistics indices. For the other three metropolitan areas, both EDGAR and the VEI inventories obtained consistent results. Therefore, the present work endorses the fact that vehicles are responsible for the more substantial contribution to PM emissions in the studied urban areas. Furthermore, the use of VEI can be representative for modeling air quality in the future.