Generating traffic flow and speed regional model data using internet GPS vehicle records.

Nowadays, many smart-phones and vehicles are equipped with Global Position System (GPS) for tracking and navigation purposes, providing an opportunity to derive highly representative local vehicular flow and estimate vehicular emissions information. Here, we report and discuss methods used to handle large volumes of such activity data, namely 124 million GPS recordings from the web page Maplink.com.br, extract high spatial resolution vehicular flow information for a vast area in South-east Brazil, and correct for bias using traffic counts observations for the same area. The method consists in filter speed and accelerations, assign buffers to the road network, aggregate speed by street, fill missing number of lanes, generate traffic flow. Methods presented here were used to inform traffic-related air quality modelling and used as part of local air pollution management activities but are also amenable to any work that would be enhanced by more locally representative or time-resolved inputs for traffic flow, e.g. traffic network management, and demand modelling. •124 million GPS observations from electronic devices were used to generate traffic flow.•Spatial bias was investigated and accounted for using independent local traffic count data.•Traffic count rescaled GPS traffic flow provide a robust description of spatial and quantitative traffic patterns.

Analysis of PM2.5 concentrations under pollutant emission control strategies in the metropolitan area of São Paulo, Brazil.

Great efforts have been made over the years to assess the effectiveness of air pollution controls in place in the metropolitan area of São Paulo (MASP), Brazil. In this work, the community multiscale air quality (CMAQ) model was used to evaluate the efficacy of emission control strategies in MASP, considering the spatial and temporal variability of fine particle concentration. Seven different emission scenarios were modeled to assess the relationship between the emission of precursors and ambient aerosol concentration, including a baseline emission inventory, and six sensitivity scenarios with emission reductions in relation to the baseline inventory: a 50% reduction in SO2 emissions; no SO2 emissions; a 50% reduction in SO2, NOx, and NH3 emissions; no sulfate (PSO4) particle emissions; no PSO4 and nitrate (PNO3) particle emissions; and no PNO3 emissions. Results show that ambient PM2.5 behavior is not linearly dependent on the emission of precursors. Variation levels in PM2.5 concentrations did not correspond to the reduction ratios applied to precursor emissions, mainly due to the contribution of organic and elemental carbon, and other secondary organic aerosol species. Reductions in SO2 emissions are less likely to be effective at reducing PM2.5 concentrations at the expected rate in many locations of the MASP. The largest reduction in ambient PM2.5 was obtained with the scenario that considered a reduction in 50% of SO2, NOx, and NH3 emissions (1 to 2 µg/m3 on average). It highlights the importance of considering the role of secondary organic aerosols and black carbon in the design of effective policies for ambient PM2.5 concentration control.

Urban pollution greatly enhances formation of natural aerosols over the Amazon rainforest.

One of the least understood aspects in atmospheric chemistry is how urban emissions influence the formation of natural organic aerosols, which affect Earth's energy budget. The Amazon rainforest, during its wet season, is one of the few remaining places on Earth where atmospheric chemistry transitions between preindustrial and urban-influenced conditions. Here, we integrate insights from several laboratory measurements and simulate the formation of secondary organic aerosols (SOA) in the Amazon using a high-resolution chemical transport model. Simulations show that emissions of nitrogen-oxides from Manaus, a city of ~2 million people, greatly enhance production of biogenic SOA by 60-200% on average with peak enhancements of 400%, through the increased oxidation of gas-phase organic carbon emitted by the forests. Simulated enhancements agree with aircraft measurements, and are much larger than those reported over other locations. The implication is that increasing anthropogenic emissions in the future might substantially enhance biogenic SOA in pristine locations like the Amazon.

WRF-SMOKE-CMAQ modeling system for air quality evaluation in São Paulo megacity with a 2008 experimental campaign data.

Atmospheric pollutants are strongly affected by transport processes and chemical transformations that alter their composition and the level of contamination in a region. In the last decade, several studies have employed numerical modeling to analyze atmospheric pollutants. The objective of this study is to evaluate the performance of the WRF-SMOKE-CMAQ modeling system to represent meteorological and air quality conditions over São Paulo, Brazil, where vehicular emissions are the primary contributors to air pollution. Meteorological fields were modeled using the Weather Research and Forecasting model (WRF), for a 12-day period during the winter of 2008 (Aug. 10th-Aug. 22nd), using three nested domains with 27-km, 9-km, and 3-km grid resolutions, which covered the most polluted cities in São Paulo state. The 3-km domain was aligned with the Sparse Matrix Operator Kernel Emissions (SMOKE), which processes the emission inventory for the Models-3 Community Multiscale Air Quality Modeling System (CMAQ). Data from an aerosol sampling campaign was used to evaluate the modeling. The PM10 and ozone average concentration of the entire period was well represented, with correlation coefficients for PM10, varying from 0.09 in Pinheiros to 0.69 in ICB/USP, while for ozone, the correlation coefficients varied from 0.56 in Pinheiros to 0.67 in IPEN. However, the model underestimated the concentrations of PM2.5 during the experiment, but with ammonium showing small differences between predicted and observed concentrations. As the meteorological model WRF underestimated the rainfall and overestimated the wind speed, the accuracy of the air quality model was expected to be below the desired value. However, in general, the CMAQ model reproduced the behavior of atmospheric aerosol and ozone in the urban area of São Paulo.

Characterization of atmospheric aerosols in the city of São Paulo, Brazil: comparisons between polluted and unpolluted periods.

The objective of this study was to determine the size and composition of atmospheric aerosols in the downtown area of the city of São Paulo, Brazil, for a polluted and an unpolluted period. Aerosols were sampled with a portable air sampler (PAS), Micro-Orifice Uniform Deposit Impactor (MOUDI), and Scanning Mobility Particle Sizer. At the study site, air quality is poor, especially during the winter, high concentrations of pollutants being emitted primarily by the light- and heavy-duty vehicle fleet. We analyzed mass, black carbon (BC), Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, Rb, Sn, Zr, and Pb. During the polluted period, diurnal PM(10) was higher than nocturnal PM(10), whereas the inverse was true during the unpolluted period. The FPM was rich in BC, S, and Pb, whereas CPM was rich in Al, Si, Ca, Ti, and Fe. Mass balance was performed by category: ammonium sulfate, sodium chloride, crustal material, BC, and other. The PAS-determined FPM was mainly BC. The MOUDI-determined FPM crustal material explained more mass than did ammonium sulfate and BC during the polluted period, whereas ammonium sulfate had the largest mass during the unpolluted period. Crustal material was the major CPM component, followed by ammonium sulfate and BC. During the unpolluted period, FPM concentrations were lower, whereas those of ammonium sulfate were relatively higher, especially at night, and particle number was inversely proportional to particle size. Aerosol growth was more intense during the polluted period.

Potential health impact of ultrafine particles under clean and polluted urban atmospheric conditions: a model-based study.

The main goal of this study was to improve the knowledge of ultrafine particle number distributions in large urban areas and also to call the attention to the importance of these particles on assessing health risks. Measurements of aerosol size distributions were performed during 2 weeks, with distinct pollutant concentrations (polluted and clean periods), on the rooftop of a building located in downtown of the megacity of São Paulo, Brazil. CO, NO(2), PM(10), SO(2), and O(3) concentrations and meteorological variables were also used. Aerosol size distribution measurements showed that geometric mean diameters of the size spectra in the polluted period are on average considerably larger than those in the clean one. Besides the fact that total number of ultrafine particles did not show significant differences, during the polluted period, geometric mean diameter was larger than during the clean one. The results of a mathematical model of particle deposition on human respiratory tract indicated a more significant effect of smaller particles fraction of the spectra, which predominate under clean atmospheric conditions. The results also indicated that urban environmental conditions usually considered good for air quality, under the criteria of low mass concentration, do not properly serve as air quality standard to very small particles. In the size range of ultrafine particles, this traditional clean atmospheric condition can offer a strong risk to pulmonary hazards, since the cleansing of the atmosphere creates good conditions to increase the concentration of nucleation mode particles.

Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil.

In the metropolitan area of São Paulo, Brazil, ozone and particulate matter (PM) are the air pollutants that pose the greatest threat to air quality, since the PM and the ozone precursors (nitrogen oxides and volatile organic compounds) are the main source of air pollution from vehicular emissions. Vehicular emissions can be measured inside road tunnels, and those measurements can provide information about emission factors of in-use vehicles. Emission factors are used to estimate vehicular emissions and are described as the amount of species emitted per vehicle distance driven or per volume of fuel consumed. This study presents emission factor data for fine particles, coarse particles, inhalable particulate matter and black carbon, as well as size distribution data for inhalable particulate matter, as measured in March and May of 2004, respectively, in the Jânio Quadros and Maria Maluf road tunnels, both located in São Paulo. The Jânio Quadros tunnel carries mainly light-duty vehicles, whereas the Maria Maluf tunnel carries light-duty and heavy-duty vehicles. In the Jânio Quadros tunnel, the estimated light-duty vehicle emission factors for the trace elements copper and bromine were 261 and 220 microg km(-1), respectively, and 16, 197, 127 and 92 mg km(-1), respectively, for black carbon, inhalable particulate matter, coarse particles and fine particles. The mean contribution of heavy-duty vehicles to the emissions of black carbon, inhalable particulate matter, coarse particles and fine particles was, respectively 29, 4, 6 and 6 times higher than that of light-duty vehicles. The inhalable particulate matter emission factor for heavy-duty vehicles was 1.2 times higher than that found during dynamometer testing. In general, the particle emissions in São Paulo tunnels are higher than those found in other cities of the world.