Patterns of mercury dispersion from local and regional emission sources, rural Central Wisconsin, USA

Abstract

Abstract. Simultaneous real-time changes in mercury (Hg) speciation-reactive gaseous Hg (RGM), elemental Hg (Hg • ), and fine particulate Hg (Hg-PM 2.5 ), were determined from June to November, 2007, in ambient air at three locations in rural Central Wisconsin. Known Hg emission sources within the airshed of the monitoring sites include: 1) a 1114 megawatt (MW) coal-fired electric utility generating station; 2) a Hg-bed chlor-alkali plant; and 3) a smaller (465 MW) coal-burning electric utility. Monitoring sites, showing sporadic elevation of Hg • , Hg-PM 2.5 , and RGM were positioned at distances of 25, 50 and 100 km northward of the larger electric utility. Median concentrations of Hg • , Hg-PM 2.5 , and RGM were 1.3-1.4 ng m −3 , 2.6-5.0 pg m −3 , and 0.6-0.8 pg m −3 , respectively. A series of RGM events were recorded at each site. The largest, on 23 September, occurred under prevailing southerly winds, with a maximum RGM value (56.8 pg m −3 ) measured at the 100 km site, and corresponding elevated SO 2 (10.4 ppbv; measured at 50 km site). The finding that RGM, Hg • , and Hg-PM 2.5 are not always highest at the 25 km site, closest to the large generating station, contradicts the idea that RGM decreases with distance from a large point source. This may be explained if: 1) the 100 km site was influenced by emissions from the chlor-alkali facility or by RGM from regional urban sources; 2) the emission stack height of the larger power plant promoted plume transport at an elevation where the Hg is carried over the closest site; or 3) RGM was being generated in the plume through oxidation of Hg • . Operational changes at each emitter since 2007 should reduce their Hg output, potentially allowing quantification of the environmental benefit in future studies.

An assessment of atmospheric mercury in the Community Multiscale Air Quality (CMAQ) model at an urban site and a rural site in the Great Lakes Region of North America

“…We show here only results from the 12 km × 12 km GLR simulations, and note that differences between the coarser 36 km × 36 km simulation and the finer 12 km × 12 km simulation were not qualitatively significant. (Table 1); both species are considerably higher at DL than in central Wisconsin observations in 2007 (Kolker et al, 2010). In contrast, CMAQ-Hg averages over three times higher over this same period, with simulated RGHg at 22.6 pg m −3 , PHg at 29.2 pg m −3 , and RHg at 52.6 pg m −3 .…”

Evaluation of discrepancy between measured and modelled oxidized mercury species

“…While atmospheric mercury reactions have been studied extensively, the impact of in-plume reactions on speciation is less known. A modelling study suggests reduction of Hg 2+ in the plume by SO 2 (Lohmann et al, 2006), but there are very few and contradictory in-plume experimental studies that neither confirm nor deny the possibility of inplume reduction with certainty Landis et al, 2009;Kolker et al, 2010;Deeds et al, 2013). As a consequence observations for oxidized and particulate mercury are required to determine the actual ratio of mercury species that will subsequently undergo tropospheric reactions.…”

Overview of receptor-based source apportionment studies for speciated atmospheric mercury

“…Back trajectories are often included in source apportionment studies to supplement the multivariate models previously described because the simulated airflows incorporate meteorological data (Hopke and Cohen, 2011). The HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model (Draxler and Rolph, 2014; has often been used in atmospheric mercury source-receptor studies (Han et al, 2004(Han et al, , 2005Lynam and Keeler, 2005;Liu et al, 2007;Rutter et al, 2007;Abbott et al, 2008;Choi et al, 2008;Li et al, 2008;Lyman and Gustin, 2008;Sprovieri and Pirrone, 2008;Cheng et al, 2009;Peterson et al, 2009;Sigler et al, 2009;Kolker et al, 2010). The HYSPLIT model simulates the transport of an air parcel by wind and estimates the position of the parcel using velocity vectors that have been spatially and temporally interpolated onto a grid (Han et al, 2005).…”