Air quality modeling to inform pollution mitigation strategies in a Latin American megacity.

Poor air quality disproportionally impacts cities in low- and middle-income countries. In Bogotá, Colombia, a metropolitan area with over 10 million inhabitants, fine particulate matter (PM2.5) levels regularly exceed air quality guidelines, leading to detrimental effects on health. Although there is public interest to improve the city's air quality, the main sources of PM2.5 pollution have not been clearly identified and the use of modeling for policy development in Bogotá has been limited. Here, we apply a modeling framework based on the Community Multiscale Air Quality Modeling System (CMAQ) to conduct seasonal simulations of air pollution in Bogotá and reveal the emissions sectors with the largest contributions to PM2.5. Based on these results, we project and compare the air quality benefits of potential pollution mitigation strategies focused on these sources. The analysis finds that resuspended dust from unpaved roads is the largest local source of PM2.5 and can contribute over 30% of seasonally-averaged concentration across the city. Vehicles, industrial activity, and unpaved road dust combined are responsible for over 60% of PM2.5 pollution in Bogotá. A scenario analysis shows that paving roads can lead to PM2.5 decreases of nearly 10 µg/m3 by 2030 in some areas of the city, but air quality will deteriorate significantly over others in the absence of additional emissions control measures. Mitigation strategies designed to target the sectors with the largest contributions to PM2.5, including road cleaning systems, controls for industrial point sources, cleaner transportation fuels, and updated vehicle fleets, can largely avert projected increases in concentrations, although the impacts of different approaches vary throughout the city. This study is the first to use a comprehensive model to determine sector contributions to air pollution and inform potential emissions control policies in Bogotá, demonstrating an approach to guide pollution management in developing cities facing comparable challenges.

Evaluation of factors influencing road dust loadings in a Latin American urban center.

Vehicle non-exhaust emissions are a major component of particle matter, including the direct wear of tires, brakes, road, and the resuspension of deposited particles. It is suggested that resuspended PM (RPM) emissions can be at the same magnitude or even larger than combustion emissions in urban centers. Factors affecting RPM can be included in four categories: road characteristics, traffic condition, land use, and meteorology. In order to study and evaluate these influencing factors, road dust less than 10 micrometers (RD10) was collected in 41 sites across Bogotá. The sampling points had diverse characteristics. RD10 levels varied between 1.0 and 45.8 mg/m2 with an average of 8.9 ± 8.4 mg/m2. Lower RD10 values were observed when vegetation density was high, pavement condition good, driving speeds fast and construction activities absent. On the contrary, RD10 increased under heavy-duty traffic influence and dry conditions. Among dust mitigation measures, management of land-use variables could be as important as traffic control and road maintenance. Implications: This study documented for the first time in Latin America dust loadings less than 10 micrometers, information that can be used to estimate resuspended particle matter emissions in the region. The influence of meteorology, traffic characteristics, road condition, and land-use variables was analyzed and quantified. The management of land-use variables could be as important as traffic control and road maintenance for road dust mitigation. Further research interests are discussed.

Air quality variations in Northern South America during the COVID-19 lockdown.

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 PM10, PM2.5, and NO2 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 NO2 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 NO2, PM10, and PM2.5 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 PM2.5 concentrations by 20 µg/m3 during the strict lockdown, and the Saharan dust event increased PM10 concentrations up to 168 µg/m3 in Bogotá, and 104 µg/m3 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.

Comparing multipollutant emissions-based mobile source indicators to other single pollutant and multipollutant indicators in different urban areas.

A variety of single pollutant and multipollutant metrics can be used to represent exposure to traffic pollutant mixtures and evaluate their health effects. Integrated mobile source indicators (IMSIs) that combine air quality concentration and emissions data have recently been developed and evaluated using data from Atlanta, Georgia. IMSIs were found to track trends in traffic-related pollutants and have similar or stronger associations with health outcomes. In the current work, we apply IMSIs for gasoline, diesel and total (gasoline + diesel) vehicles to two other cities (Denver, Colorado and Houston, Texas) with different emissions profiles as well as to a different dataset from Atlanta. We compare spatial and temporal variability of IMSIs to single-pollutant indicators (carbon monoxide (CO), nitrogen oxides (NOx) and elemental carbon (EC)) and multipollutant source apportionment factors produced by Positive Matrix Factorization (PMF). Across cities, PMF-derived and IMSI gasoline metrics were most strongly correlated with CO (r = 0.31-0.98), while multipollutant diesel metrics were most strongly correlated with EC (r = 0.80-0.98). NOx correlations with PMF factors varied across cities (r = 0.29-0.67), while correlations with IMSIs were relatively consistent (r = 0.61-0.94). In general, single-pollutant metrics were more correlated with IMSIs (r = 0.58-0.98) than with PMF-derived factors (r = 0.07-0.99). A spatial analysis indicated that IMSIs were more strongly correlated (r > 0.7) between two sites in each city than single pollutant and PMF factors. These findings provide confidence that IMSIs provide a transferable, simple approach to estimate mobile source air pollution in cities with differing topography and source profiles using readily available data.

Bayesian-based ensemble source apportionment of PM2.5.

A Bayesian source apportionment (SA) method is developed to provide source impact estimates and associated uncertainties. Bayesian-based ensemble averaging of multiple models provides new source profiles for use in a chemical mass balance (CMB) SA of fine particulate matter (PM2.5). The approach estimates source impacts and their uncertainties by using a short-term application of four individual SA methods: three receptor-based models and one chemical transport model. The method is used to estimate two seasonal distributions of source profiles that are used in SA for a long-term PM2.5 data set. For each day in a long-term PM2.5 data set, 10 source profiles are sampled from these distributions and used in a CMB application, resulting in 10 SA results for each day. This formulation results in a distribution of daily source impacts rather than a single value. The average and standard deviation of the distribution are used as the final estimate of source impact and a measure of uncertainty, respectively. The Bayesian-based source impacts for biomass burning correlate better with observed levoglucosan (R(2) = 0.66) and water-soluble potassium (R(2) = 0.63) than source impacts estimated using more traditional methods and more closely agrees with observed total mass. The Bayesian approach also captures the expected seasonal variation of biomass burning and secondary impacts and results in fewer days with sources having zero impact. Sensitivity analysis found that using non-informative prior weighting performed better than using weighting based on method-derived uncertainties. This approach can be applied to long-term data sets from speciation network sites of the United States Environmental Protection Agency (U.S. EPA). In addition to providing results that are more consistent with independent observations and known emission sources being present, the distributions of source impacts can be used in epidemiologic analyses to estimate uncertainties associated with the SA results.

Health risk represented by inhaling polycyclic aromatic hydrocarbons (PAH) during daily commuting involving using a high traffic flow route in Bogotá / Exposición a hidrocarburos aromáticos policíclicos durante desplazamientos diarios en una vía de alto flujo vehicular en Bogotá

Objective Assessing the risk to health by inhaling particles and particle-bound PAH during daily commuting along a high traffic flow route/corridor in Bogotá. Methods A van was equipped with a PAS2000 photo-electric sensor for real-time measurement of particle-bound PAH and a Dust Trakfor monitoring PM10 concentration; it drove along typical commuting routes in the city. Exposure to particles and particle-bound PAH was assessed by using an inhalation intake model. Results A similar trend was observed for both PM10 and PAH concentration, indicating that traffic was the same source for both contaminants. Extreme PM10 and PAH inhalation concentrations were recorded every time direct bus and microbus emissions were measured by the van. Inhalation model results indicated that exposure was significantly greater when using a venues having mixed traffic use (i.e. buses, microbuses, passenger vehicles, motorcycles) compared to using roads where the TransMilenio system (articulated buses) had been implemented. Conclusions The results may support evaluating bus drivers, commuters and bike users' exposure to toxic compounds in the city.(AU)

Objetivo El presente estudio buscó estimar la exposición a material particulado e Hidrocarburos Aromáticos Poli cíclicos (HAP) asociados a partículas durante el desplazamiento diario de la población en una vía de alto flujo vehicular en Bogotá. Método Se instaló un equipo "dust-track" para la medición de partículas y un equipo fotoeléctrico "PAS2000" para la medición de los HAP, ambos en un vehículo tipo van que replicó el desplazamiento diario de personas de la casa al trabajo y viceversa. Con el fin de valorar la exposición personal a estos contaminantes según el tipo de vía se usó un modelo de inhalación. Resultados Se observó una tendencia similar entre la concentración del material particulado menor a 10 micrómetros (PM10) y los HAP, indicando que ambos provienen del tráfico principalmente. El modelo de inhalación mostró que las concentraciones a las que está expuesta una persona son mayores en vías con diferentes tipologías de vehículos (carros, buses y busetas, camiones, motocicletas) que en vías de uso exclusivo público para el sistema TransMilenio. Conclusiones Los resultados de este estudio son útiles en la evaluación de la población a contaminantes tóxicos del aire y pueden ser utilizados para apoyar políticas públicas destinadas al mejoramiento de la calidad del aire.(AU)

Health risk represented by inhaling polycyclic aromatic hydrocarbons (PAH) during daily commuting involving using a high traffic flow route in Bogotá.

OBJECTIVE: Assessing the risk to health by inhaling particles and particle-bound PAH during daily commuting along a high traffic flow route/corridor in Bogotá. METHODS: A van was equipped with a PAS2000 photo-electric sensor for real-time measurement of particle-bound PAH and a Dust Trakfor monitoring PM10 concentration; it drove along typical commuting routes in the city. Exposure to particles and particle-bound PAH was assessed by using an inhalation intake model. RESULTS: A similar trend was observed for both PM10 and PAH concentration, indicating that traffic was the same source for both contaminants. Extreme PM10 and PAH inhalation concentrations were recorded every time direct bus and microbus emissions were measured by the van. Inhalation model results indicated that exposure was significantly greater when using a venues having mixed traffic use (i.e. buses, microbuses, passenger vehicles, motorcycles) compared to using roads where the TransMilenio system (articulated buses) had been implemented. CONCLUSIONS: The results may support evaluating bus drivers, commuters and bike users' exposure to toxic compounds in the city.

Development of outcome-based, multipollutant mobile source indicators.

Multipollutant indicators of mobile source impacts are developed from readily available CO, NOx, and elemental carbon (EC) data for use in air quality and epidemiologic analysis. Two types of outcome-based Integrated Mobile Source Indicators (IMSI) are assessed. The first is derived from analysis of emissions of EC, CO, and NOx such that pollutant concentrations are mixed and weighted based on emission ratios for both gasoline and diesel vehicles. The emission-based indicators (IMSI(EB)) capture the impact of mobile sources on air quality estimated from receptor models and their uncertainty is comparable to measurement and source apportionment uncertainties. The IMSI(EB) have larger correlation between two different receptor sites impacted by traffic than single pollutants, suggesting they are better indicators of the local impact ofmobile sources. A sensitivity analysis of fractions of pollutants in a two-pollutant mixture and the inclusion in an epidemiologic model is conducted to develop a second set of indicators based on health outcomes. The health-based indicators (IMSI(HB)) are weighted combinations of CO, NOx, and EC pairs that have the lowest P value in their association with cardiovascular disease emergency department visits, possibly due to their better spatial representativeness. These outcome-based, multipollutant indicators can provide support for the setting of multipollutant air quality standards and other air quality management activities.