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Copyright of Revista Ciência e Natura is the property of Revista Ciencia e Natura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Ambiente e Água is the property of Revista Ambiente e Agua and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Sociedade & Natureza is the property of Sociedade & Natureza and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research aimed to evaluate the impacts of maximum rainfall in watershed of the Rio Doce in the probability of flooding alert flow in the municipality of Colatina, ES, Brazil, using Logit model. To this, it were considered the rainfall stations of the locations of Aimorés, Tumiritinga, Conselheiro Pena and Resplendor in the Minas Gerais state and Baixo Guandu, Itaguaçu, Itarana and Colatina in the state of Espírito Santo, beyond fluviometric station in Colatina municipality, during the period from 01/01/1986 to 31/12/2014. The results of the logistic regression model shown significant impacts of the maximums rainfall in Tumiritinga, Aimorés, Baixo Guandu and Colatina in the probability of occurrence of episodes of flood warning in Colatina, ES. Moreover, it was observed that in periods with higher volumes of rain (spring and summer), the chance of occurrence of a warning flow event in Colatina increased significantly. Lastly, it is hoped that this article can support actions aimed at flood control. [ABSTRACT FROM AUTHOR]

With population growth, economic development and the increase of motorized fleet, the pollution sources without proper control are making worse the quality of the environment. The increase of the current vehicles fleet in Santa Maria de Jetibá has contributed to raising the concentration of pollutants in the city atmosphere. In this context, this study aimed to conduct a study of emissions from leakage of light motor vehicles in the municipality of Santa Maria de Jetibá - ES in 2009. The methodology was based on methods for the preparation of vehicle emissions inventories of the Environmental Protection Agency of the United States and the Primeiro Inventário Nacional de Emissões Atmosférica por Veículos Automotores Rodoviários prepared by Ministério do Meio Ambiente. From the results of this research, there is a considerable reduction in pollutant emissions under study since 1990. This decrease can be attributed to the suitability of vehicle manufacturing technologies and due to the implementation of Law number 8723 of October 28,1993, laying down detailed rules for the issuance of automotive pollutants. [ABSTRACT FROM AUTHOR]

This work shows a time series study of the average monthly flow using stochastic model of the SARIMA class (Seasonal Autoregressive Moving Average). The hydrology model is an important tool in the planning and management of water resources programs. This article aimed to analyse, comparatively, time series models, as well as to realize forecasting of average monthly flow taken Doce River as watershed, it's situated in Colatina municipality/ES. The applied methodology in this work was based in the proposal by Box and Jenkins. The identification of the order of the models was made by graphic analysis of the autocorrelation function (ACF) e partial autocorrelation (PACF). Among possible models was selected those that obtained minor value of the following parameters: AIC, BIC, EQM and EAM. The chosen model was SARIMA(1,1,1)(1,1,2)12, that represented well the dynamic of the series studied and showed the best numerical results in both calibration and validation stages. The chosen model was used to make forecasting to months of 2014 and 2015. The average monthly flow forecasting showed, obtained with the considered models, can be used to analyse hydrology regime of the Doce river, mainly to the critical months of 2015, as well as it can propose preventive measures to reduce both environmental and economic impacts in this municipality. [ABSTRACT FROM AUTHOR]

This work aimed to model and forecast the average daily concentration of inhalable particulate matter (PM10) using the SARIMAX model, in the Greater Vit�ria Region (RGV), ES, Brazil, for the period from January 1st, 2008 to December 31th, 2018. For this, time series of PM10 concentration and meteorological parameters wind speed (V), relative humidity (U), precipitation (PP), temperature (T), solar radiation (I) and atmospheric pressure (P) were considered. These parameters were obtained from the State Environmental Institute (IEMA), being chosen the Laranjeiras, Carapina, Jardim Camburi, Enseada do Su�, Vit�ria (Centro), Ibes and Vila Capixaba stations to the study of prediction and forecasting. According to performance indicators, SARIMAX models, for most seasons, have been presented as good models for making predictions and forecasts of air quality in the localities. Regarding the prediction of regular air quality events, in general, the SARIMAX models stood out when compared to the SARIMA and ARMA models. Among the meteorological variables evaluated, V, U, PP and T stand out as predictor variables of PM10 concentrations and assumed a decisive role in improving the performance of the prediction models. [ABSTRACT FROM AUTHOR]