Formation of semivolatile inorganic aerosols in the Mexico City Metropolitan Area during the MILAGRO campaign

Abstract

Abstract. One of the most challenging tasks for chemical transport models (CTMs) is the prediction of the formation and partitioning of the major semi-volatile inorganic aerosol components (nitrate, chloride, ammonium) between the gas and particulate phases. In this work the PMCAMx-2008 CTM, which includes the recently developed aerosol thermodynamic model ISORROPIA-II, is applied in the Mexico City Metropolitan Area in order to simulate the formation of the major inorganic aerosol components. The main sources of SO 2 (such as the Miguel Hidalgo Refinery and the Francisco Perez Rios Power Plant) in the Mexico City Metropolitan Area (MCMA) are located in Tula, resulting in high predicted PM 1 (particulate matter with diameter less than 1 µm) sulfate concentrations (over 25 µg m −3 ) in that area. The average predicted PM 1 nitrate concentrations are up to 3 µg m −3 (with maxima up to 11 µg m −3 ) in and around the urban center, mostly produced from local photochemistry. The presence of calcium coming from the Tolteca area (7 µg m −3 ) as well as the rest of the mineral cations (1 µg m −3 potassium, 1 µg m −3 magnesium, 2 µg m −3 sodium, and 3 µg m −3 calcium) from the Texcoco Lake resulted in the formation of a significant amount of aerosol nitrate in the coarse mode with concentrations up to 3 µg m −3 over these areas. PM 1−10 (particulate matter with diameter between 1 and 10 µm) chloride is also high and its concentration exceeds 2 µg m −3 in Texcoco Lake. PM 1 ammonium concentrations peak at Correspondence to: S. N. Pandis (spyros@chemeng.upatras.gr) the center of Mexico City (2 µg m −3 ) and the Tula vicinity (2.5 µg m −3 ). The performance of the model for the major inorganic PM components (sulfate, ammonium, nitrate, chloride, sodium, calcium, and magnesium) is encouraging. At the T0 measurement site, located in the Mexico City urban center, the average measured values of PM 1 sulfate, nitrate, ammonium, and chloride are 3.5 µg m −3 , 3.5 µg m −3 , 2.1 µg m −3 , and 0.36 µg m −3 , respectively. The corresponding predicted values are 3.7 µg m −3 , 2.7 µg m −3 , 1.7 µg m −3 , and 0.25 µg m −3 . High sulfate concentrations are associated with the transport of sulfate from the Tula vicinity, while in periods where southerly winds are dominant; the concentrations of sulfate are low. The underprediction of nitrate can be attributed to the underestimation of OH levels by the model during the early morning. Ammonium is sensitive to the predicted sulfate concentrations and the nitrate levels. The performance of the model is also evaluated against measurements taken from a suburban background site (T1) located north of Mexico City. The average predicted PM 2.5 (particulate matter with diameter less than 2.5 µm) sulfate, nitrate, ammonium, chloride, sodium, calcium, and magnesium are 3.3, 3.2, 1.4, 0.5, 0.3, 1.2, and 0.15 µg m −3 , respectively. The corresponding measured concentrations are 3.7, 2.9, 1.5, 0.3, 0.4, 0.6, and 0.15 µg m −3 . The overprediction of calcium indicates a possible overestimation of its emissi...

Impact of agricultural emission reductions on fine-particulate matter and public health

“…Tsimpidi et al (2007) showed that a 50 % reduction of NH 3 emissions would lead to a 4 and 9 % decrease in PM 2.5 over the eastern USA in July and January, respectively. The reduction of NH 3 emissions was found to be the most effective PM 2.5 control measure for the winter period over the eastern USA compared to similar reductions of SO 2 , NO x and VOC emissions (Pinder et al, 2008;Tsimpidi et al, 2007Tsimpidi et al, , 2008Karydis et al, 2011). Megaritis et al (2013) and Bessagnet et al (2014) found that over Europe the reduction of NH 3 emissions is the most effective control strategy used to mitigate PM 2.5 in both summer and winter, mainly due to a significant decrease of ammonium nitrate.…”

Effects of mineral dust on global atmospheric nitrate concentrations

“…Several studies in the past have shown that the simulation of these effects, especially in areas where dust or sea salt comprises a significant portion of total particulate matter, can considerably improve model predictions (Dentener et al, 1996;Gong et al, 1997;Jacobson, 1999;Jacob, 2000;Song and Carmichael, 2001;Moya et al, 2002;Bian and Zender, 2003;Laskin et al, 2005;Hodzic et al, 2006;Kallos et al, 2007;Astitha and Kallos, 2009;Athanasopoulou et al, 2008Athanasopoulou et al, , 2010Fountoukis et al, 2009;Karydis et al, 2010Karydis et al, , 2011aTsyro et al, 2011;Wang et al, 2012;Im, 2013;Trump et al, 2015). According to their findings, including marine and crustal species in models can substantially affect the phase partitioning of nitrate aerosols.…”

“…Few of these studies have applied a hybrid approach for aerosol thermodynamics, which combines the dynamic calculation of mass transfer to coarse-mode particles with an equilibrium approach for the fine-mode particles, and found that mineral dust and sea salt can considerably affect the size distribution of aerosol nitrate (Athanasopoulou et al, 2008(Athanasopoulou et al, , 2010Karydis et al, 2010Karydis et al, , 2011aTrump et al, 2015). The presence of mineral cations can also affect the aerosol ammonium concentrations due to thermodynamic interactions with the remainder ions in the aqueous phase (Karydis et al, 2010(Karydis et al, , 2011a. Furthermore, heterogeneous chemistry occurring on dust particles can also act as a source for sulfate (Wang et al, 2012).…”

Global impact of mineral dust on cloud droplet number concentration

“…Anthropogenic NO − 3 and SO 2− 4 mainly contribute to the chemical aging of dust over continents, while sea-salt-derived Cl − is more important over oceans (Sullivan et al, 2007;Fountoukis et al, 2009;Dall'Osto et al, 2010;Tobo et al, 2010;Karydis et al, 2011b;Bougiatioti et al, 2016b;Weber et al, 2016). Apart from the gas-phase composition, the chemical processing of dust also depends on its chemical composition and thus on the source region (Sullivan et al, 2009;Karydis et al, 2016).…”