In this paper, we analyze the variability of the ozone concentration over São Paulo Macrometropolis, as well the factors, which determined the tendency observed in the last two decades. Time series of hourly ozone concentrations measured at 16 automated stations from an air quality network from 1996 to 2017 were analyzed. The temporal variability of ozone concentrations exhibits well-defined daily and seasonal patterns. Ozone presents a significant positive correlation between the number of cases (thresholds of 100–160 μg m⁻³) and the fuel sales of gasohol and diesel. The ozone concentrations do not exhibit significant long-term trends, but some sites present positive trends that occurs in sites in the proximity of busy roads and negative trends that occurs in sites located in residential areas or next to trees. The effect of atmospheric process of transport and ozone formation was analyzed using a quantile regression model (QRM). This statistical model can deal with the nonlinearities that appear in the relationship of ozone and other variables and is applicable to time series with non-normal distribution. The resulting model explains 0.76% of the ozone concentration variability (with global coefficient of determination R¹ = 0.76) providing a better representation than an ordinary least square regression model (with coefficient of determination R² = 0.52); the effect of radiation and temperature are the most critical in determining the highest ozone quantiles.

In this work, we report the ongoing implementation of online-coupled aerosol–cloud microphysical–radiation interactions in the Brazilian global atmospheric model (BAM) and evaluate the initial results, using remote-sensing data for JFM 2014 and JAS 2019. Rather than developing a new aerosol model, which incurs significant overheads in terms of fundamental research and workforce, a simplified aerosol module from a preexisting global aerosol–chemistry–climate model is adopted. The aerosol module is based on a modal representation and comprises a suite of aerosol microphysical processes. Mass and number mixing ratios, along with dry and wet radii, are predicted for black carbon, particulate organic matter, secondary organic aerosols, sulfate, dust, and sea salt aerosols. The module is extended further to include physically based parameterization for aerosol activation, vertical mixing, ice nucleation, and radiative optical properties computations. The simulated spatial patterns of surface mass and number concentrations are similar to those of other studies. The global means of simulated shortwave and longwave cloud radiative forcing are comparable with observations with normalized mean biases ≤11% and ≤30%, respectively. Large positive bias in BAM control simulation is enhanced with the inclusion of aerosols, resulting in strong overprediction of cloud optical properties. Simulated aerosol optical depths over biomass burning regions are moderately comparable. A case study simulating an intense biomass burning episode in the Amazon is able to reproduce the transport of smoke plumes towards the southeast, thus showing a potential for improved forecasts subject to using near-real-time remote-sensing fire products and a fire emission model. Here, we rely completely on remote-sensing data for the present evaluation and restrain from comparing our results with previous results until a complete representation of the aerosol lifecycle is implemented. A further step is to incorporate dry deposition, in-cloud and below-cloud scavenging, sedimentation, the sulfur cycle, and the treatment of fires.

Air quality models are tools capable to predict the physical and chemical processes that occur in atmosphere affecting the atmospheric composition, such as wind advection, turbulent diffusion, wet and dry deposition, chemical reactions, photolysis, anthropogenic and biogenic emission processes. These models need input data containing information about atmosphere (usually from a global atmospheric model), terrestrial data (usually for the models maintainer) and emissions (that comes from air quality pollution inventories). EmissV is a code written to create emissions input for these atmospheric models.

One risk associated with rocket‐launching activities is the inhalation of exhaust gases, which can occur either in the vicinity of the launch pad or, depending on the way these gases are transported and dispersed through the atmosphere, at locations far from the launch pad. Immediately after the ignition of the rocket motors, a large, hot cloud is formed near the ground, composed of carbon monoxide (CO) and carbon dioxide (CO₂), hydrogen chloride (HCl) and also particulate material composed of aluminium oxide (Al₂O₃) in the case of rockets driven by a solid propellant. The Weather Research and Forecasting Model (WRF), an atmospheric mesoscale model, was modified in the present study to simulate the transport, dispersion and chemistry mechanisms (with the addition of reactions involving HCl and other chlorinated compounds) of these gases released during a launch. Simulations of the dispersion of effluents were performed for different rainfall regimes (dry and rainy periods) as well as for atmospheric thermal stability (day‐ and night‐time conditions) for a specific rocket (the Veículo Lançador de Satélites, VLS) at Brazil's Alcântara Launch Center (Centro de Lançamento de Alcântara, CLA). The results show that the most critical levels of HCl and CO occurred on the launch pad (Setor de Preparação de Lançamento, SPL) from the time of the launch (t₀) to 10 min. The village of Alcântara, which is 9 km from the launch pad, could be affected according to the wind direction. The concentration of HCl reached a level in the range of 2200–3800 ppmv for both dry and wet periods respectively, and these concentrations can be very hazardous to humans.

Eva3dm is a package designed to support the evaluation of 3-dimensional physical models (particularly, weather and air quality models) against observation data in order to quantify different errors and bias present in the model results.