No abstract

Lockdown measures led to air pollution decrease in several countries around the world such as China and India, whereas other regions experimented an increase in pollutant concentrations. Northern South America (NSA) was one of those areas where pollution changed during lockdown due to high fire activity. This study aims to analyze, for the first time in NSA, the behavior of selected criteria air pollutants during the implementation of the SARS-CoV-2 lockdown in two high populated cities of the region: Bogotá and Medellín in Colombia. A set of tools including surface measurements, as well as satellite and modeled data were used. 24-hour average concentrations of PM 10 , PM 2.5 , and NO 2 were collected from air quality stations for the lockdown period ranging from February 21 to June 30, 2020. The Copernicus Atmosphere Monitoring Service (CAMS) was used to analyze the fire flux OC as a biomass burning (BB) indicator, and tropospheric NO 2 concentrations were retrieved from TROPOMI. The HYSPLIT model was used to analyze back trajectories and fire data were obtained from MODIS sensor measurements. Our analysis shows short-term background NO 2 , PM 10 , and PM 2.5 concentration reductions of 60%, 44%, and 40%, respectively, for the strict lockdown; and 62%, 58%, and 69% for the relaxed lockdown. Corresponding long-term reductions were of 50%, 32%, and 9% for the strict lockdown; and 37%, 29%, and 19% for the relaxed lockdown. Regional BB increased PM 2.5 concentrations by 20 μg/m 3 during the strict lockdown, and the Saharan dust event increased PM 10 concentrations up to 168 μg/m 3 in Bogotá, and 104 μg/m 3 in Medellín, bringing an additional risk of morbidity and mortality for population. Regional BB has several causes that need to be properly managed to benefit local air quality improvement plans. Future cleaner transport policies equivalent to reduced lockdown mobility could bring pollution close to WHO guidelines.

An ensemble-trained chemical mass balance (CMB) approach is developed for particulate matter (PM) source apportionment (SA), particularly for use in health studies. The approach uses results from a short-term emission-based chemical transport model (CTM) and multiple receptor-based approaches. Ensemble results have less day-to-day variation in source impacts and fewer biases between observed and estimated PM2.5 mass compared to the original receptor model results. Ensemble results show increases in road dust, biomass burning, and coal impacts, but secondary organic carbon (SOC) impacts decrease. These results, along with observations, are then used to obtain new source profiles. Two sets of new source profiles based on ensemble results in summer (July 2001 and winter (January 2002) were developed, and used in separate CMB applications for a 12-month data set of daily PM2.5 measurements at the Atlanta, GA, Jefferson Street site. Results show that ensemble-trained CMB approaches, using both summer profiles and winter profiles, effectively reduce day-to-day variability of source impact estimates by reducing fewer days of zero impact from sources known to be present as compared to traditional receptor modeling, suggesting improved results.