Urban air pollution is a matter of concern due to its health hazards and the continuous population growth exposed to it at different urban areas worldwide. Nowadays, more than 55% of the world population live in urban areas. One of the main challenges to guide pollution control policies is related to pollutant source assessment. In this line, U.S. Environmental Protection Agency's Positive Matrix Factorization (EPA-PMF) has been extensively employed worldwide as a reference model for quantification of source contributions. However, EPA-PMF presents issues associated to source identification and discrimination due to the collinearities among the source tracers. Multi-Isotopic Fingerprints (MIF) have demonstrated good resolution for source discrimination, since urban sources are characterized by specific isotopic signatures. Source quantification based on total aerosol mass is the main limitation of MIF. This study reports strategies for PMF and MIF combination to improve source identification/discrimination and its quantification in urban areas. We have three main findings: (1) cross-validation of PMF source identification based on Pb and Zn isotopic fingerprints, (2) source apportionment in the MIF model for total PM mass, and (3) new insights into potential Zn isotopic signatures of biomass burning and secondary aerosol. We support future studies on the improvement of isotopic fingerprints database of sources based on diverse elements or compounds to boost advances of MIF model applications in atmospheric sciences.

Since 2001, four emission measurement campaigns have been conducted in multiple traffic tunnels in the megacity of Saõ Paulo, Brazil, an area with a fleet of more than 7 million vehicles running on fuels with high biofuel contents: gasoline + ethanol for lightduty vehicles (LDVs) and diesel + biodiesel for heavy-duty vehicles (HDVs). Emission factors for LDVs and HDVs were calculated using a carbon balance method, the pollutants considered including nitrogen oxides (NO x ), carbon monoxide (CO), and sulfur dioxide, as well as carbon dioxide and ethanol. From 2001 to 2018, fleet-average emission factors for LDVs and HDVs, respectively, were found to decrease by 4.9 and 5.1% per year for CO and by 5.5 and 4.2% per year for NO x . These reductions demonstrate that regulations for vehicle emissions adopted in Brazil in the last 30 years improved air quality in the megacity of Saõ Paulo significantly, albeit with a clear delay. These findings, especially those for CO, indicate that official emission inventories underestimate vehicle emissions. Here, we demonstrated that the adoption of emission factors calculated under real-world conditions can dramatically improve air quality modeling in the region.

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The role of particulate matter (PM) in the COVID-19 pandemic is currently being discussed by the scientific community. Long-term (years) exposure to PM is known to affect human health by increasing susceptibility to viral infections as well as to the development of respiratory and cardiovascular symptoms. In the short-term (days to months), PM has been suggested to assist airborne viral transmission. However, confounding factors such as urban mobility prevent causal conclusions. In this study, we explore short-term relationships between PM concentrations and the evolution of COVID-19 cases in a number of cities in the United States of America. We focus on the role of PM in facilitating viral transmission in early stages of the pandemic. We analyzed PM concentrations in two particle size ranges, <2.5 μm, and between 10 and 2.5 μm (PM2.5 and PM10 respectively) as well as carbon monoxide (CO) and nitrogen dioxide (NO2). Granger causality analysis was employed to evaluate instantaneous and lagged effects of pollution in peaks of COVID-19 new daily cases in each location. The effect of pollution in shaping the disease spread was evaluated by correlating the logistic growth rate of accumulated cases with pollutants concentrations for a range of time lags and accumulation windows. PM2.5 shows the most significant results in Granger causality tests in comparison with the other pollutants. We found a strong and significant association between PM2.5 concentrations and the growth rate of accumulated cases between the 1st and 18th days after the report of the infection, peaking at the 8th day. By comparing results of PM2.5 with PM10, CO and NO2 we rule out confounding effects associated with mobility. We conclude that PM2.5 is not a first order effect in the cities considered; however, it plays a significant role in facilitating the COVID-19 transmission.

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