A Global-Scale Mineral Dust Equation.

A robust method to estimate mineral dust mass in ambient particulate matter (PM) is essential, as the dust fraction cannot be directly measured but is needed to understand dust impacts on the environment and human health. In this study, a global-scale dust equation is developed that builds on the widely used Interagency Monitoring of Protected Visual Environments (IMPROVE) network's "soil" formula that is based on five measured elements (Al, Si, Ca, Fe, and Ti). We incorporate K, Mg, and Na into the equation using the mineral-to-aluminum (MAL) mass ratio of (K2O + MgO + Na2O)/Al2O3 and apply a correction factor (CF) to account for other missing compounds. We obtain region-specific MAL ratios and CFs by investigating the variation in dust composition across desert regions. To calculate reference dust mass for equation evaluation, we use total-mineral-mass (summing all oxides of crustal elements) and residual-mass (subtracting non-dust species from total PM) approaches. For desert dust in source regions, the normalized mean bias (NMB) of the global equation (within ±1%) is significantly smaller than the NMB of the IMPROVE equation (-6% to 10%). For PM2.5 with high dust content measured by the IMPROVE network, the global equation estimates dust mass well (NMB within ±5%) at most sites. For desert dust transported to non-source regions, the global equation still performs well (NMB within ±2%). The global equation can also represent paved road, unpaved road, and agricultural soil dust (NMB within ±5%). This global equation provides a promising approach for calculating dust mass based on elemental analysis of dust.

Simulation of airborne trace metals in fine particulate matter over North America.

Trace metal distributions are of relevance to understand sources of fine particulate matter (PM2.5), PM2.5-related health effects, and atmospheric chemistry. However, knowledge of trace metal distributions is lacking due to limited ground-based measurements and model simulations. This study develops a simulation of 12 trace metal concentrations (Si, Ca, Al, Fe, Ti, Mn, K, Mg, As, Cd, Ni and Pb) over continental North America for 2013 using the GEOS-Chem chemical transport model. Evaluation of modeled trace metal concentrations with observations indicates a spatial consistency within a factor of 2, an improvement over previous studies that were within a factor of 3-6. The spatial distribution of trace metal concentrations reflects their primary emission sources. Crustal element (Si, Ca, Al, Fe, Ti, Mn, K) concentrations are enhanced over the central US from anthropogenic fugitive dust and over the southwestern U.S. due to natural mineral dust. Heavy metal (As, Cd, Ni and Pb) concentrations are high over the eastern U.S. from industry. K is abundance in the southeast from biomass burning and high concentrations of Mg is observed along the coast from sea spray. The spatial pattern of PM2.5 mass is most strongly correlated with Pb, Ni, As and K due to their signature emission sources. Challenges remain in accurately simulating observed trace metal concentrations. Halving anthropogenic fugitive dust emissions in the 2011 National Air Toxic Assessment (NATA) inventory and doubling natural dust emissions in the default GEOS-Chem simulation was necessary to reduce biases in crustal element concentrations. A fivefold increase of anthropogenic emissions of As and Pb was necessary in the NATA inventory to reduce the national-scale bias versus observations by more than 80 %, potentially reflecting missing sources.

Space-time trends of PM2.5 constituents in the conterminous United States estimated by a machine learning approach, 2005-2015.

Particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) is a complex mixture of chemical constituents emitted from various emission sources or through secondary reactions/processes; however, PM2.5 is regulated mostly based on its total mass concentration. Studies to identify the impacts on climate change, visibility degradation and public health of different PM2.5 constituents are hindered by limited ground measurements of PM2.5 constituents. In this study, national models were developed based on random forest algorithm, one of machine learning methods that is of high predictive capacity and able to provide interpretable results, to predict concentrations of PM2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) across the conterminous United States from 2005 to 2015 at the daily level. The random forest models achieved high out-of-bag (OOB) R2 values at the daily level, and the mean OOB R2 values were 0.86, 0.82, 0.71 and 0.75 for sulfate, nitrate, OC and EC, respectively, over 2005-2015. The long-term temporal trends of PM2.5 sulfate, nitrate, OC and EC predictions agreed well with their corresponding ground measurements. The annual mean of predicted PM2.5 sulfate and EC levels across the conterminous United States decreased substantially from 2005 to 2015; while concentrations of predicted PM2.5 nitrate and OC decreased and fluctuated during the study period. The annual prediction maps captured the characterized spatial patterns of the PM2.5 constituents. The distributions of annual mean concentrations of sulfate and nitrate were generally regional in the extent that sulfate decreased from east to west smoothly with enhancement in California and nitrate had higher concentration in Midwest, Metro New York area, and California. OC and EC had regional high concentrations in the Southeast and Northwest as well as localized high levels around urban centers. The spatial patterns of PM2.5 constituents were consistent with the distributions of their emission sources and secondary processes and transportation. Hence, the national models developed in this study could provide supplementary evaluations of spatio-temporal distributions of PM2.5 constituents with full time-space coverages in the conterminous United States, which could be beneficial to assess the impacts of PM2.5 constituents on radiation budgets and visibility degradation, and support exposure assessment for regional to national health studies at county or city levels to understand the acute and chronic toxicity and health impacts of PM2.5 constituents, and consequently provide scientific evidence for making targeted and effective regulations of PM2.5 pollution.

Origin of Fine Particulate Carbon in the Rural United States.

Carbonaceous compounds are a significant component of fine particulate matter and haze in national parks and wilderness areas where visibility is protected, i.e., class I areas (CIAs). The Regional Haze Rule set the goal of returning visibility in CIAs on the most anthropogenically impaired days to natural by 2064. To achieve this goal, we need to understand contributions of natural and anthropogenic sources to the total fine particulate carbon (TC). A Lagrangian chemical transport model was used to simulate the 2006-2008 contributions from various source types to measured TC in CIAs and other rural lands. These initial results were incorporated into a hybrid model to reduce systematic biases. During summer months, fires and vegetation-derived secondary organic carbon together often accounted for >75% of TC. Smaller contributions, <20%, from area and mobile sources also occurred. During the winter, contributions from area and mobile sources increased, with area sources accounting for half or more of the TC in many regions. The area emissions were likely primarily from residential and industrial wood combustion. Different fire seasons were evident, with the largest contributions during the summer when wildfires occur and smaller contributions during the spring and fall when prescribed and agricultural fires regularly occur.