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Comparison of lightning flash data from the National Lightning Detection Network (NLDN) and from the World Wide Lightning Location Network (WWLLN) over the contiguous United States (CONUS) for the 2016–2018 period reveals temporally and spatially varying flash rates that would influence lightning NOx (LNOx) production due to known detection efficiency differences especially during summer months over land (versus over ocean). However, the lightning flash density differences between the two networks show persistent seasonal patterns over geographical regions (e.g., land versus ocean). Since the NLDN data are considered to have higher accuracy (lightning detection with >95% efficiency), we developed scaling factors for the WWLLN flash data based on the ratios of WWLLN to NLDN flash data over time (months of year) and space. In this study, sensitivity simulations using the Community Multiscale Air Quality (CMAQ) model are performed utilizing the original data sets (both NLDN and WWLLN) and the scaled WWLLN flash data for LNOx production over the CONUS. The model performance of using the different lightning flash datasets for ambient O3 and NOx mixing ratios that are directly impacted by LNOx emissions and the wet and dry deposition of oxidized nitrogen species that are indirectly impacted by LNOx emissions is assessed based on comparisons with ground-based observations, vertical profile measurements, and satellite products. During summer months, the original WWLLN data produced less LNOx emissions (due to its lower lightning detection efficiency) compared to the NLDN data, which resulted in less improvement in model performance than the simulation using NLDN data as compared to the simulation without any LNOx emissions. However, the scaled WWLLN data produced LNOx estimates and model performance comparable with the NLDN data, suggesting that scaled WWLLN may be used as a substitute for the NLDN data to provide LNOx estimates in air quality models when the NLDN data are not available (e.g., due to prohibitive cost or lack of spatial coverage). [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the details of the Atmospheric Model Evaluation Tool (AMET) v1.1 created by scientists in the Atmospheric Modeling and Analysis Division (AMAD) of the U.S. Environmental Protection Agency (EPA). AMET was first developed to evaluate the performance of the 5th Generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological model output and was later extended to include capabilities for evaluating output data from the Community Multiscale Air Quality (CMAQ) model as well. AMET is designed to leverage several open-source software packages that are used in combination to 1) pair the modeled and observed values in time and space, 2) store these paired datasets in an easily accessible and searchable database and 3) access and analyze these data using a statistical package. Through this process, AMET is able to provide a convenient method for evaluating meteorological and air quality model predictions. The use of a searchable, relational database allows the entire dataset to be quickly subset into only those data that are of the most interest for the current analysis, a process that is often tedious and time consuming without the use of a database. In addition to common summary statistics (e.g. RMSE, bias, and correlation), several of the many analysis features available in AMET include scatter plots, time series plots, box plot and spatial plots as part of operational model evaluation. Additionally, several unique analysis functions are also available in AMET, and the system provides a framework within which users may extend the current functionality for their own custom analyses. While AMET was designed to work specifically with MM5, WRF and CMAQ model output, it could easily be modified to work with output data from other meteorological and air quality models. [Copyright &y& Elsevier]

Abstract: This paper compares spatial and seasonal variations and temporal trends in modeled and measured concentrations of sulfur and nitrogen compounds in wet and dry deposition over an 18-year period (1988–2005) over a portion of the northeastern United States. Substantial emissions reduction programs occurred over this time period, including Title IV of the Clean Air Act Amendments of 1990 which primarily resulted in large decreases in sulfur dioxide (SO2) emissions by 1995, and nitrogen oxide (NOx) trading programs which resulted in large decreases in warm season NOx emissions by 2004. Additionally, NOx emissions from mobile sources declined more gradually over this period. The results presented here illustrate the use of both operational and dynamic model evaluation and suggest that the modeling system largely captures the seasonal and long-term changes in sulfur compounds. The modeling system generally captures the long-term trends in nitrogen compounds, but does not reproduce the average seasonal variation or spatial patterns in nitrate. [ABSTRACT FROM AUTHOR]

The western United States experienced a record‐breaking wildfire season in 2020. This study quantifies the contribution of wildfire emissions to the exceedances of health‐based National Ambient Air Quality Standard (NAAQS) for fine particles (PM2.5) by comparing two CMAQ simulations, with and without wildfire emissions. During August to October 2020, western wildfires contributed 23% of surface PM2.5 in the contiguous US (CONUS), with a larger contribution in Pacific Coast (43%) and Mountain Region (42%). Consequently, wildfires were the primary contributor to the 3,720 observed exceedances. The wildfire influence peaked on September 14th, 2020, when 273 exceedances were recorded and wildfire emissions contributed 41%, 81%, and 72% to surface PM2.5 concentrations in the CONUS, Pacific Coast, and Mountain Region, respectively. Our finding highlights the predominating influence of wildfires on air quality, and potentially human health, that is expected to grow with increasing fire activities, while anthropogenic emissions decrease. Plain Language Summary: In the summer of 2020, the western United States experienced a record‐breaking number of wildfires. We looked into the effects of these wildfires on air quality, through the lens of the exceedances of health‐based National Ambient Air Quality Standards (NAAQS) for fine particles (PM2.5), which are associated with the bulk of health risks posed by air pollution. We found that in 2020, the western wildfires contributed 23% of surface PM2.5 pollution during August to October in the contiguous United States. The contribution is much bigger in the Pacific Coast (43%) and the Mountain Region (42%). Consequently, the wildfires were the primary contributor to the 3,720 exceedances of PM2.5 NAAQS. Our finding highlights the dominating influence of wildfire emissions on air quality and potentially on human health. Key Points: Wildfire emissions contribute 23% of surface PM2.5 concentration in the contiguous United States during the summer of 2020Wildfires were the primary contributor to the 3,720 exceedances of National Ambient Air Quality Standard for PM2.5 during the 2020 summerOur finding highlights the predominating influence of wildfires on air quality during the 2020 wildfire season [ABSTRACT FROM AUTHOR]