The study described in this manuscript analyzed the effects of quarantine and social distancing policies implemented due to the Coronavirus Disease 2019 (COVID-19) pandemic on air pollution levels in four western megacities: São Paulo in Brazil; Paris in France; and Los Angeles and New York in the United States. The study investigated the levels of four air pollutants—Carbon monoxide (CO), Ozone (O₃), Fine Particulate (PM_2.5) and Nitrogen dioxide (NO₂)—during the month of March 2020, compared to 2015–2019, in the urban air of these metropolitan areas, controlling for meteorological variables. Results indicated reductions in the levels of PM_2.5, CO and NO₂, with reductions of the latter two showing statistical significance. In contrast, tropospheric ozone levels increased, except in Los Angeles. The beneficial health effects of cleaner air might also help prevent deaths caused by the epidemic of COVID-19 in megacities by diminishing pressure on hospitals and health equipment. Future actions for the re-starting of non-essential economic activities in these cities should take into consideration the overall importance of health for the individual, as well as for societies.

The urban population of South America has grown at 1.05%/yr, greater urbanization increasing problems related to air pollution. In most large cities in South America, there has been no continuous long‐term measurement of regulated pollutants. One exception is São Paulo, Brazil, where an air quality monitoring network has been in place since the 1970s. In this paper, we used an air quality‐based approach to determine pollutant trends for emissions of carbon monoxide (CO), nitrogen oxides (NO_x), ozone (O₃), and coarse particulate matter (PM₁₀), mostly from mobile sources, in the Metropolitan Region of São Paulo for the 2000–2013 period. Mobile sources included light‐duty vehicles (LDVs, comprising gasoline‐ or ethanol‐powered cars and motorcycles) and heavy‐duty vehicles (HDVs, comprising diesel‐powered trucks and buses). Pollutant concentrations for mobile source emissions were measured and correlated with fuel sales by the emission factors. Over the 2000–2013 period, concentrations of NO_x, CO, and PM₁₀ decreased by 0.65, 0.37, and 0.71% month⁻¹, respectively, whereas sales of gasoline, ethanol, and diesel increased by 0.26, 1.96, and 0.38% month⁻¹, respectively. LDVs were the major mobile source of CO, whereas LDVs were the major source of NO_x and PM₁₀. Increases in fuel sales and in the corresponding traffic volume were partially offset by decreases in pollutant concentrations. Between 2000 and 2013, there was a sharp (−5 ppb month⁻¹) decrease in the concentrations of LDV‐emitted CO, together with (less dramatic) decreases in the concentrations of HDV‐emitted NO_x and PM₁₀ (−0.25 and −0.09 ppb month⁻¹, respectively). Variability was greater for HDV‐emitted NO_x and PM₁₀ (R = −0.47 and −0.41, respectively) than for LDV‐emitted CO (R = −0.72). We draw the following conclusions: the observed concentrations of LDV‐emitted CO decreased at a sharper rate than did those of HDV‐emitted NO_x and PM₁₀; mobile source contributions to O₃ formation varied significantly, LDVs making a greater contribution during the 2000–2008 period, whereas HDVs made a greater contribution during the 2009–2013 period, and decreases in NO_x emissions resulted in increases in O₃ observations.

This article provides a new methodology for estimating fuel consumption and emissions by enabling a correct comparison between freight transportation modes. The approach is developed and integrated as a part of an intelligent transportation system dealing with goods movement. A key issue is related to energy consumption ratios and consequent CO₂ emissions. Energy consumption ratios are often used based on transport demand. However, including other ratios based on transport supply can be useful. Furthermore, it is important to indicate which factors are associated with variations in energy consumption and emissions; especially of interest are parameters that have a higher incidence and order of magnitude, in order to fairly compare and understand the difference between transport modes and sub-modes. The study finds that the use of an energy consumption equation can improve the quality of the estimates. The study proposes that coefficients that define the energy consumption equation should be tested to determine market niches and sources of improvement in energy consumption according to the category of vehicles, fuel types used, and classes of products transported.