Abstract. 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 , SO 2 , O 3 , PM 2.5 , PM 10 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.

Abstract. How a changing energy matrix for electricity production affects air quality is considered for an urban region in a tropical, forested environment. Manaus, the largest city in the central Amazon Basin of Brazil, is in the process of changing its energy matrix for electricity production from fuel oil and diesel to natural gas over an approximately 10-year period, with a minor contribution by hydropower. Three scenarios of urban air quality, specifically afternoon ozone concentrations, were simulated using the Weather Research and Forecasting (WRF-Chem) model. The first scenario used fuel oil and diesel for electricity production, which was the reality in 2008. The second scenario was based on the fuel mix from 2014, the most current year for which data were available. The third scenario considered nearly complete use of natural gas for electricity production, which is the anticipated future, possibly for 2018. For each case, inventories of anthropogenic emissions were based on electricity generation, refinery operations, and transportation. Transportation and refinery operations were held constant across the three scenarios to focus on effects of power plant fuel switching in a tropical context. The simulated NO x and CO emissions for the urban region decrease by 89 and 55 %, respectively, after the complete change in the energy matrix. The results of the simulations indicate that a change to natural gas significantly decreases maximum afternoon ozone concentrations over the population center, reducing ozone by > 70 % for the most polluted days. The sensitivity of ozone concentrations to the fuel switchover is consistent with a NO xlimited regime, as expected for a tropical forest having high emissions of biogenic volatile organic compounds, high water vapor concentrations, and abundant solar radiation. There are key differences in a shifting energy matrix in a tropical, forested environment compared to other world environments. Policies favoring the burning of natural gas in place of fuel oil and diesel have great potential for ozone reduction and improved air quality for growing urban regions located in tropical, forested environments around the world.

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The main objective of this study was to investigate the trends on average and extreme events in time series of daily precipitation from 1980 to 2010 in the Paraná River basin, Brazil. The nonparametric Mann–Kendall test was applied to detect monotonic trend in the precipitation series. The occurrence of extreme values was analysed based on three generalized extreme values (GEV) models: Model 1 (stationary), Model 2 (non‐stationary for location parameter), and Model 3 (non‐stationary for location and scale parameters). The GEV parameters were estimated by the Generalized Maximum Likelihood method (GMLE) and for the non‐stationary models, the parameters were estimated as linear functions of time. To choose the most suitable model, the maximum likelihood ratio test (D) was used. From the results observed at the monthly scale, it was possible to infer that the months with the highest probability of an extreme weather event occurrence are February (climates Aw and Cfa), July (Cfa and Cfb), and October (Aw, Cfa, and Cfb). Approximately 90% of the 1,112 stations presented no trend regarding the GEV parameters. The non‐stationarity showed by other stations (Models 2 and 3) might be associated with several factors, such as the alteration of land use due to the north expansion of the agricultural border of the Paraná River basin.

Abstract: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 • 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 m s −1 in the wind speed, and approximately 1 • C in temperature, showing the importance of a better representation of the urban structure and the SVF database improvement.