This paper evaluates the contributions of the emissions from mobile, stationary and biogenic sources on air pollution in the Amazon rainforest by using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. The analyzed air pollutants were CO, NO_x, SO₂, O₃, PM_2. 5, PM₁₀ and volatile organic compounds (VOCs). Five scenarios were defined in order to evaluate the emissions by biogenic, mobile and stationary sources, as well as a future scenario to assess the potential air quality impact of doubled anthropogenic emissions. The stationary sources explain the highest concentrations for all air pollutants evaluated, except for CO, for which the mobile sources are predominant. The anthropogenic sources considered resulted an increasing in the spatial peak-temporal average concentrations of pollutants in 3 to 2780 times in relation to those with only biogenic sources. The future scenario showed an increase in the range of 3 to 62 % in average concentrations and 45 to 109 % in peak concentrations depending on the pollutant. In addition, the spatial distributions of the scenarios has shown that the air pollution plume from the city of Manaus is predominantly transported west and southwest, and it can reach hundreds of kilometers in length.

The Upper Paraná River Basin (UPRB) has undergone many rapid land use changes in recent decades, due to accelerating population growth. Thus, the prediction of water resources has crucial importance in improving planning and sustainable management. This paper presents a large-scale hydrological modelling of the UPRB, using the Soil and Water Assessment Tool (SWAT) model. The model was calibrated and validated for 78 outlets, over a 32-year simulation period between 1984 and 2015. The results and the comparison between observed and simulated values showed that after the calibration process, most of the outlets performed to a satisfactory level or better in all objective functions analyzed with 86%, 92%, 76%, 88%, and 74% for Percent bias, Coefficient of determination, Nash-Sutcliffe efficiency, Kling-Gupta efficiency, and the Ratio of Standard deviation of observations to root mean square error, respectively. The model output provided in this work could be used in further simulations, such as the evaluation of the impacts of land use change or climate change on river flows of the Upper Paraná Basin.

The sky view factor (SVF) is an important radiometric parameter for assessing the canopy energy budget of urban areas. There are several methods to determine the SVF observationally. The most common is taking a photo with a digital camera equipped with a fish-eye lens and then converting ratio of sky area to canopy area into SVF. However, most urban canopy models use this variable as derived from idealized canopy geometry. To evaluate the effect of inputting observed SVFs in numerical models, we evaluated a mesoscale model’s performance in reproducing surface wind and surface temperature when subjected to different ways of SVF prescription. The studied area was the Metropolitan Area of São Paulo (MASP) in Brazil. Observed SVFs were obtained for 37 sites scattered all over the MASP. Three simulations, A, B, and C, with different SVF and aspect-ratio prescriptions, were performed to analyze the effect of SVF on the urban canopy parameterization: Simulation A (standard) used the original formulation of the Town Energy Budget (TEB) model, computing the SVFs from the aspect-ratios; Simulation B used the observed SVFs, but keeps aspect-ratios as original; and Simulation C used the aspect-ratios computed from observed SVFs. The results show that in general inputting observed SVFs improves the model capability of reproducing temperature at surface level. The comparison of model outputs with data of regular meteorological stations shows that the inclusion of observed values of SVFs enhances model performance, reducing the RMSE index by up to 3 ${}^{\circ }$C. In this case, the model is able to better reproduce the expected effects in the wind field, and consequently the temperature advection, of the urban boundary layer to a large urban area. The result of Simulation C shows that the surface wind and temperature intensity for all urban types is higher than those of Simulation A, because of the lower values of the aspect ratio. The urban type with high density of tall buildings increase up to 1 $\mathrm{m}{\mathrm{s}}^{-1}$ in the wind speed, and approximately 1 ${}^{\circ }$C in temperature, showing the importance of a better representation of the urban structure and the SVF database improvement.

Fruticulture is a prominent component of Brazilian agriculture. Studies have shown that climatic variability and its impact on the physiological development of plant species are fundamental for planning the agricultural calendar, resource conservation, and sustainable management of production. In this context, one of the first criterion for planting a crop is agroclimatic zoning, since it provides information on climate-related risks and aids decision-making and agricultural planning. The objective of this study was to carry out climatic risk zoning for avocado (Persea americana Mill.) in the basin of Paraná River III, Paraná State, Brazil. Meteorological data from 43 stations, from 1976 to 2018, were used. The climatic risk analysis was based on the requirements of the avocado for precipitation, water balance, average annual temperature, and frost tolerance. Statistical and geoprocessing techniques ensured full regional coverage of data and contributed to decision-making. The results identified favorable climatic conditions for all climatic variables in the western part of the river basin. Despite water deficits in some months, rainfall and water balance were not restrictive for avocado production in the region. Avocado tree cultivation is not recommended in the eastern part of the basin, where there is a considerable risk of frost.