Development of an Optimum Tracer Set for Apportioning Emissions of Individual Power Plants Using Highly Time-Resolved Measurements and Advanced Receptor Modeling

In previous studies, 11 elements (Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Se, and Zn) were determined in 30-minute aerosol samples collected with the University of Maryland Semicontinuous Elements in Aerosol Sampler (SEAS; Kidwell and Ondov, 2001, 2004; SEAS-II) in several locations in which air quality is influenced by emissions from coal- or oil-fired power plants. At this time resolution, plumes from stationary high temperature combustion sources are readily detected as large excursions in ambient concentrations of elements emitted by these sources (Pancras et al. ). Moreover, the time-series data contain intrinsic information on the lateral diffusion of the plume (e.g., {sigma}{sub y}), which Park et al. (2005 and 2006) have exploited in their Pseudo-Deterministic Receptor Model (PDRM), to calculate emission rates of SO{sub 2} and 11 elements (mentioned above) from four individual coal- and oil-fired power plants in the Tampa Bay area. In the current project, we proposed that the resolving power of source apportionment methods might be improved by expanding the set of maker species and that there exist some optimum set of marker species that could be used. The ultimate goal was to determine the utility of using additional elements to better identify and isolate contributions of individual power plants to ambient levels of PM and its constituents. And, having achieved better resolution, achieve, also, better emission rate estimates. In this study, we optimized sample preparation and instrumental protocols for simultaneous analysis of 28 elements in dilute slurry samples collected with the SEAS with a new state-of-the-art Thermo-Systems, Inc., X-series II, Inductively Coupled Plasma Mass Spectroscopy (ICP-MS), and reanalyzed the samples previously collected in Tampa during the modeling period studied by Park et al. (2005) in which emission rates from four coal- and oil-fired power plants affected air quality at the sampling site. In the original model, Park et al. (2005), included 6 sources. Herein, we reassessed the number of contributing sources in light of the new data. A comprehensive list of sources was prepared and both our Gaussian Plume model and PMF were used to identify and predict the relative strengths of source contributions at the receptor sites. Additionally, PDRM was modified to apply National Inventory Emissions, Toxic Release Inventory, and Chemical Mass Balance source profile data to further constrain solutions. Both the original Tampa data set (SO{sub 2} plus 11 elements) and the new expanded data set (SO{sub 2} plus 23 elements) were used to resolve the contributions of particle constituents and PM to sources using Positive Matrix Factorization (PMF) and PDRM

Fingerprints of the COVID-19 economic downturn and recovery on ozone anomalies at high-elevation sites in North America and western Europe

With a few exceptions, most studies on tropospheric ozone (O3) variability during and following the COrona VIrus Disease (COVID-19) economic downturn focused on high-emission regions or urban environments. In this work, we investigated the impact of the societal restriction measures during the COVID-19 pandemic on surface O3 at several high-elevation sites across North America and western Europe. Monthly O3 anomalies were calculated for 2020 and 2021, with respect to the baseline period 2000–2019, to explore the impact of the economic downturn initiated in 2020 and its recovery in 2021. In total, 41 high-elevation sites were analyzed: 5 rural or mountaintop stations in western Europe, 19 rural sites in the western US, 4 sites in the western US downwind of highly polluted source regions, and 4 rural sites in the eastern US, plus 9 mountaintop or high-elevation sites outside Europe and the United States to provide a “global” reference. In 2020, the European high-elevation sites showed persistent negative surface O3 anomalies during spring (March–May, i.e., MAM) and summer (June–August, i.e., JJA), except for April. The pattern was similar in 2021, except for June. The rural sites in the western US showed similar behavior, with negative anomalies in MAM and JJA 2020 (except for August) and MAM 2021.The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation program (grant agreement no. 654109). Surface O3 measurements at Summit are made possible via the US National Science Foundation Office of Polar Programs and their contract with Battelle Arctic Research Operations (contract no. 49100420C0001). Owen R. Cooper, Kai-Lan Chang, Irina Petropavlovskikh, and Peter Effertz were supported by a NOAA cooperative agreement (grant no. NA22OAR4320151). The publication costs of this research have been partially supported by the European Commission under the Horizon 2020 research and innovation framework program through ACTMO-ACCESS Integrating Activity (grant agreement no. 101008004)

An improved pseudo-deterministic receptor model (iPDRM) to apportion ambient PM constituents to sources in Tampa, FL

In 2005, Park et al., developed a new Pseudo-Deterministic Receptor Model (PDRM) to apportion SO2 and ambient particulate matter (PM) constituents to local sources near Tampa Bay. Ambient pollutant measurements were fit to products of emission rates and dispersion factors constrained with a Gaussian plume model for individual sources. In our study, the original samples were reanalyzed by ICPMS for 10 additional elements to improve the resolving power. Chemical mass balance (CMB) terms were added to PDRM to allow fitting of background aerosol sources. More accurate curvilinear plume trajectories were computed to predict arrival times in both surface and aloft layers. This allowed application of the PDRM to complicated meteorological conditions, e.g. wind shifts. Predicted emission rates for particle-bound elements were constrained using chemical compositional information obtained from published source profiles for generic source types. Constraints applied to emissions of known tracer species allowed the "conditioning" of dispersion factor temporal profiles to tracer species concentration profiles. This enabled the model to apportion pollutants to individual sources with intermittent emissions, the omission of which in Park et al. lead to significant residuals. Excellent fits were obtained for all modeled pollutants: 14 of 22 species have Normalized Mean Square Errors (NMSE) < 2.5%, and 21 of 22 have values < 8%. These were improved for SO 2 and 8 of 10 elements (by 7-35% for Al, Cu, Ni, Pb, and Zn) modeled by Park et al. Our predicted emission rates are in much better agreement with chemical compositions for generic source types. Key results include: (1) predicted SO2 contributions to ambient levels from a small, lead battery recycling plant were reduced from 50-59% at its peak influence to a more reasonable 2-4%, (2) Pb/Zn ratios from that plant increased from 1.0 to 734 and better agree with published ratios of 67-440, (3) predicted Ni emission rates for one of the oil-fired power plants (OFPP) was increased by 100-fold (larger than Park's), and now better agrees with its published National Emissions Inventory (NEI) emission rate and with X/Ni ratios for generic OFPP emissions derived from EPA's SPECIATE database, and (4) our predicted emission rates for hazardous air pollutants and toxics from power plants agree within a factor of 5 for ∼75% of the annual emission rates reported in the NEI and Toxic Release Inventories (TRI). This suggests that NEI and TRI data provide good qualitative emission estimates, but should not be treated as accurate in a predictive model to quantify source emissions. It was also observed that the TRI values for As emission rates from coal-fired power plants are more accurate that their NEI values

Fingerprints of the COVID-19 economic downturn and recovery on ozone anomalies at high-elevation sites in North America and Western Europe

Abstract. With a few exceptions, most studies on tropospheric ozone (O3) variability during and following the COVID-19 economic downturn focused on high-emission regions or urban environments. In this work, we investigated the impact of the societal restriction measures during the COVID-19 pandemic on surface O3 at several high-elevation sites across North America and Western Europe. Monthly O3 anomalies were calculated for 2020 and 2021, with respect to the baseline period 2000–2019, to explore the impact of the economic downturn initiated in 2020 and its recovery in 2021. In total, 41 high-elevation sites were analyzed: 5 rural or mountaintop stations in Western Europe, 19 rural sites in the Western US, 4 sites in the Western US downwind of highly polluted source regions, 4 rural sites in the eastern US, plus 9 mountaintop or high-elevation sites outside Europe and the United States to provide a “global” reference. In 2020, the European high-elevation sites showed persistent negative surface O3 anomalies during spring (March–May, i.e., MAM) and summer (June–August, i.e., JJA), except for April. The pattern was similar in 2021, except for June. The rural sites in the Western US showed similar behavior, with negative anomalies in MAM and JJA 2020 (except for August), and MAM 2021. The JJA 2021 seasonal average was influenced by strong positive anomalies in July, due to large and widespread wildfires across the Western US. The polluted sites in the Western US showed negative O3 anomalies during MAM 2020, and a slight recovery in 2021, resulting in a positive average anomaly for MAM 2021 and a pronounced month-to-month variability in JJA 2021 anomalies. The Eastern US sites were also characterized by below average O3 for both MAM and JJA 2020, while in 2021 the negative values exhibited an opposite structure compared to the Western US sites, which were influenced by wildfires. Concerning the rest of the World, a global picture could not be drawn, as the sites, spanning a range of different environments, did not show consistent anomalies, with a few sites not experiencing any notable variation. Moreover, we also compared our surface anomalies to the variability of mid-tropospheric O3 detected by the IASI satellite instrument. Negative anomalies were observed by IASI, consistent with published satellite and modeling studies, suggesting that the anomalies can be largely attributed to the reduction of O3 precursor emissions in 2020

The potential of high temporal resolution automatic measurements of PM2.5 composition as an alternative to the filter-based manual method used in routine monitoring

•Under the EU Air Quality Directive (AQD) 2008/50/EC member states are required to undertake routine monitoring of PM2.5 composition at background stations. The AQD states for PM2.5 speciation this should include at least: nitrate (NO3−), sulfate (SO42−), chloride (Cl−), ammonium (NH4+), sodium (Na+), potassium (K+), magnesium (Mg2+), calcium (Ca2+), elemental carbon (EC) and organic carbon (OC). Until 2017, it was the responsibility of each country to determine the methodology used to report the composition for the inorganic components of PM2.5. In August 2017 a European standard method of measurement of PM2.5 inorganic chemical components (NO3−, SO42−, Cl−, NH4+, Na+, K+, Mg2+, Ca2+) as deposited on filters (EN16913:2017) was published. From August 2019 this then became the European standard method. This filter method is labour-intensive and provides limited time resolution and is prone to losses of volatile compounds. There is therefore increasing interest in the use of alternative automated methods. For example, the UK reports hourly PM2.5 chemical composition using the Monitor for AeRosols and Gases in Ambient air (MARGA, Metrohm, NL). This study is a pre-assessment review of available data to demonstrate if or to what extent equivalence is possible using either the MARGA or other available automatic methods, including the Aerosol Chemical Speciation Monitor (ACSM, Aerodyne Research Inc. US) and the Ambient Ion Monitor (AIM, URG, US). •To demonstrate equivalence three objectives were to be met. The first two objectives focused on data capture and were met by all three instruments. The third objective was to have less than a 50% expanded uncertainty compared to the reference method for each species. Analysis of this objective was carried out using existing paired datasets available from different regions around the world. It was found that the MARGA (2006–2019 model) had the potential to demonstrate equivalence for all species in the standard, though it was only through a combination of case studies that it passed uncertainty criteria. The ACSM has the potential to demonstrate equivalence for NH4+, SO42−, and in some conditions NO3−, but did not for Cl− due to its inability to quantify refractory aerosol such as sea salt. The AIM has the potential for NH4+, NO3−, SO42−, Cl− and Mg2+. Future investigations are required to determine if the AIM could be optimised to meet the expanded uncertainty criterion for Na+, K+ and Ca2+. •The recommendation is that a second stage to demonstrate equivalence is required which would include both laboratory and field studies of the three candidate methods and any other technologies identified with the potential to report the required species