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Nitrous acid (HONO) is a main precursor of hydroxyl radicals (OH), which contribute to the formation of numerous secondary air pollutants in the troposphere. Despite its importance in atmospheric chemistry, HONO chemistry has not been fully incorporated into many chemical transport models (CTMs). Due to the lack of atmospheric HONO processes, CTM simulations often tend to underestimate atmospheric mixing ratios of HONO. This study was undertaken because simulations with the current Community Multiscale Air Quality (CMAQ) model have a strong tendency to underestimate the HONO mixing ratio. In search of missing sources of atmospheric HONO, we attempted to sequentially incorporate the following potential HONO sources and processes into the CMAQ modeling framework: (i) gas-phase HONO reactions, (ii) traffic HONO emissions, (iii) soil HONO emissions, (iv) heterogeneous HONO production on the surfaces of aerosols, (v) heterogeneous HONO formation on tree leaf and building surfaces, and (vi) photolysis reactions of particulates and deposited HNO3/nitrates called “renoxification”. The simulation performances of the modified CMAQ models were then evaluated by comparing the modeled HONO mixing ratios with the HONO mixing ratios observed at the Olympic Park station in Seoul, South Korea. When HONO processes were fully added to the CMAQ model, average daily HONO mixing ratios increased from 0.06 to 1.18 ppb. The daytime HONO mixing ratios produced from the CMAQ model run with a full account of atmospheric HONO processes were found to be in better agreement with observations than those from the original CMAQ model (CMAQv5.2.1) runs with improved statistical metrics (e.g., the index of agreement (IOA) increased from 0.59 to 0.68, while the mean bias (MB) decreased dramatically from −0.57 to −0.34 ppb). In addition, we investigated the contributions of individual atmospheric HONO processes to HONO mixing ratios, as well as the impacts of HONO atmospheric processes on the concentrations of other atmospheric species in South Korea. All these issues are also discussed in this paper.

Nitrogen dioxide (NO2) is a precursor of ozone (O3) and fine particulate matter (PM2.5) – two pollutants that are above regulatory guidelines in many cities. Bringing urban areas into compliance of these regulatory standards motivates an understanding of the distribution and sources of NO2 through observations and simulations. The TRACER-AQ campaign, conducted in Houston, Texas, in September 2021, provided a unique opportunity to compare observed NO2 columns from ground-, airborne-, and satellite-based spectrometers. In this study, we investigate how these observational datasets compare and simulate column NO2 using WRF–CAMx with fine resolution (444 × 444 m2) comparable to the airborne column measurements. We compare WRF-simulated meteorology to ground-level monitors and find good agreement. We find that observations from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument were strongly correlated (r2 = 0.79) to observations from Pandora spectrometers with a slight high bias (normalized mean bias (NMB) = 3.4 %). Remote sensing observations from the TROPOspheric Monitoring Instrument (TROPOMI) were generally well correlated with Pandora observations (r2 = 0.73) with a negative bias (NMB = −22.8 %). We intercompare different versions of TROPOMI data and find similar correlations across three versions but slightly different biases (from −22.8 % in v2.4.0 to −18.2 % in the NASA MINDS product). Compared with Pandora observations, the WRF–CAMx simulation had reduced correlation (r2 = 0.34) and a low bias (−21.2 %) over the entire study region. We find particularly poor agreement between simulated NO2 columns and GCAS-observed NO2 columns in downtown Houston, an area of high population and roadway densities. These findings point to a potential underestimate of NOx emissions (NOx = NO + NO2) from sources associated with the urban core of Houston, such as mobile sources, in the WRF–CAMx simulation driven by the Texas state inventory, and further investigation is recommended.

Elevated ground-level ozone concentrations have emerged as a major environmental issue in China. Nitrogen oxide (NOx) is a key precursor to ozone formation. Although control strategies aimed at reducing NOx emissions from conventional combustion sources are widely recognized, soil NOx emissions (mainly as NO) due to microbial processes have received little attention. The impact of soil NO emissions on ground-level ozone concentration is yet to be evaluated. This study estimated soil NO emissions in China using the Berkeley–Dalhousie Soil NOx Parameterization (BDSNP) algorithm. A typical modeling approach was used to quantify the contribution of soil NO emissions to surface ozone concentration. The brute-force method (BFM) and the Ozone Source Apportionment Technology (OSAT) implemented in the Comprehensive Air Quality Model with Extensions (CAMx) were used. The total soil NO emissions in China for 2018 were estimated to be 1157.9 Gg N, with an uncertainty range of 715.7–1902.6 Gg N. Spatially, soil NO emissions are mainly concentrated in Central China, North China, Northeast China, the northern Yangtze River Delta (YRD), and the eastern Sichuan Basin, with distinct diurnal and monthly variations that are mainly affected by the temperature and timing of fertilizer application. Both the BFM and OSAT results indicate a substantial contribution of soil NO emissions to the maximum daily 8 h (MDA8) ozone concentrations by 8.0–12.5 µg m−3 on average for June 2018, with the OSAT results being consistently higher than the BFM results. The results also showed that soil NO emissions led to a relative increase in ozone exceedance days by 10.5 %–43.5 % for selected regions. Reducing the soil NO emissions resulted in a general decrease in monthly MDA8 ozone concentrations, and the magnitude of ozone reduction became more pronounced as reductions increased. However, even with complete reductions in soil NO emissions, approximately 450.3 million people are still exposed to unhealthy ozone levels, necessitating multiple control policies at the same time. This study highlights the importance of soil NO emissions for ground-level ozone concentrations and the potential for reducing NO emissions as a future control strategy for ozone mitigation in China.

The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a valuable source of information to monitor the NOx emissions that adversely affect air quality. We conduct a series of experiments using a 4×4 km2 Comprehensive Air Quality Model with Extensions (CAMx) simulation during April–September 2019 in eastern Texas to evaluate the multiple challenges that arise from reconciling the NOx emissions in model simulations with TROPOMI. We find an increase in NO2 (+17 % in urban areas) when transitioning from the TROPOMI NO2 version 1.3 algorithm to the version 2.3.1 algorithm in eastern Texas, with the greatest difference (+25 %) in the city centers and smaller differences (+5 %) in less polluted areas. We find that lightning NOx emissions in the model simulation contribute up to 24 % of the column NO2 in the areas over the Gulf of Mexico and 8% in Texas urban areas. NOx emissions inventories, when using locally resolved inputs, agree with NOx emissions derived from TROPOMI NO2 version 2.3.1 to within 20 % in most circumstances, with a small NOx underestimate in Dallas–Fort Worth (−13 %) and Houston (−20 %). In the vicinity of large power plant plumes (e.g., Martin Lake and Limestone) we find larger disagreements, i.e., the satellite NO2 is consistently smaller by 40 %–60 % than the modeled NO2, which incorporates measured stack emissions. We find that TROPOMI is having difficulty distinguishing NO2 attributed to power plants from the background NO2 concentrations in Texas – an area with atmospheric conditions that cause short NO2 lifetimes. Second, the NOx/NO2 ratio in the model may be underestimated due to the 4 km grid cell size. To understand ozone formation regimes in the area, we combine NO2 column information with formaldehyde (HCHO) column information. We find modest low biases in the model relative to TROPOMI HCHO, with −9 % underestimate in eastern Texas and −21 % in areas of central Texas with lower biogenic volatile organic compound (VOC) emissions. Ozone formation regimes at the time of the early afternoon overpass are NOx limited almost everywhere in the domain, except along the Houston Ship Channel, near the Dallas/Fort Worth International airport, and in the presence of undiluted power plant plumes. There are likely NOx-saturated ozone formation conditions in the early morning hours that TROPOMI cannot observe and would be well-suited for analysis with NO2 and HCHO from the upcoming TEMPO (Tropospheric Emissions: Monitoring Pollution) mission. This study highlights that TROPOMI measurements offer a valuable means to validate emissions inventories and ozone formation regimes, with important limitations.

Numerical air quality models (AQMs) have been applied more frequently over the past decade to address diverse scientific and regulatory issues associated with deteriorated air quality in China. Thorough evaluation of a model's ability to replicate monitored conditions (i.e., a model performance evaluation or MPE) helps to illuminate the robustness and reliability of the baseline modeling results and subsequent analyses. However, with numerous input data requirements, diverse model configurations, and the scientific evolution of the models themselves, no two AQM applications are the same and their performance results should be expected to differ. MPE procedures have been developed for Europe and North America, but there is currently no uniform set of MPE procedures and associated benchmarks for China. Here we present an extensive review of model performance for fine particulate matter (PM2.5) AQM applications to China and, from this context, propose a set of statistical benchmarks that can be used to objectively evaluate model performance for PM2.5 AQM applications in China. We compiled MPE results from 307 peer-reviewed articles published between 2006 and 2019, which applied five of the most frequently used AQMs in China. We analyze influences on the range of reported statistics from different model configurations, including modeling regions and seasons, spatial resolution of modeling grids, temporal resolution of the MPE, etc. Analysis using a random forest method shows that the choices of emission inventory, grid resolution, and aerosol- and gas-phase chemistry are the top three factors affecting model performance for PM2.5. We propose benchmarks for six frequently used evaluation metrics for AQM applications in China, including two tiers – “goals” and “criteria” – where goals represent the best model performance that a model is currently expected to achieve and criteria represent the model performance that the majority of studies can meet. Our results formed a benchmark framework for the modeling performance of PM2.5 and its chemical species in China. For instance, in order to meet the goal and criteria, the normalized mean bias (NMB) for total PM2.5 should be within 10 % and 20 %, while the normalized mean error (NME) should be within 35 % and 45 %, respectively. The goal and criteria values of correlation coefficients for evaluating hourly and daily PM2.5 are 0.70 and 0.60, respectively; corresponding values are higher when the index of agreement (IOA) is used (0.80 for goal and 0.70 for criteria). Results from this study will support the ever-growing modeling community in China by providing a more objective assessment and context for how well their results compare with previous studies and to better demonstrate the credibility and robustness of their AQM applications prior to subsequent regulatory assessments.

Rapid sulfate formation is recognized as a key characteristic of severe winter haze in China. However, air quality models tend to underestimate sulfate formation during heavy haze periods, and heterogeneous formation pathways have been proposed as promising mechanisms to reduce gaps between observation and model simulation. In this study, we implemented a reactive SO2 uptake mechanism through the SO2+NO2 heterogeneous reactions in the Comprehensive Air Quality Model with Extensions (CAMx) to improve simulation of sulfate formation in the Yangtze River Delta (YRD) region. Parameterization of the SO2+NO2 heterogeneous reactions is based on observations in Beijing and considered both the impact of relative humidity and aerosol pH on sulfate formation. Ammonia is reported to be critical for the formation of secondary inorganic aerosols. Estimation of ammonia emissions is usually associated with large uncertainties and models tend to underestimate ammonia concentrations substantially. Sensitivity tests were conducted to evaluate the influence of the SO2+NO2 heterogeneous reactions as well as ammonia emissions on modeled sulfate concentrations during a period with several heavy haze episodes in the YRD region. Base case model results show large underestimation of sulfate concentrations by 36 % under polluted conditions in the YRD region. Adding the SO2+NO2 heterogeneous reactions or doubling ammonia emissions alone leads to slight model improvement (∼6 %) on simulated sulfate concentrations in the YRD region. However, model performance significantly improved when both the SO2+NO2 heterogeneous reactions and doubled ammonia emissions were included in the simulation: predicted sulfate concentrations during polluted periods increased from 23.1 µg m−3 in the base scenario to 29.1 µg m−3 (representing an increase of 26 %). Aerosol pH is crucial for the SO2+NO2 heterogeneous reactions, and our calculated aerosol pH is always acidic and increased by 0.7 with doubled ammonia emissions. Modeling results also show that this reactive SO2 uptake mechanism enhanced sulfate simulations by 1 to 5 µg m−3 for the majority of the eastern and central parts of China, with more than 20 µg m−3 increase in sulfate concentrations over the northeastern plain. These findings suggest that the SO2+NO2 heterogeneous reactions could be potentially important for sulfate formation in the YRD region as well as other parts of China. Further studies are needed to constrain the uncertainties associated with the parameterization of the SO2+NO2 heterogeneous reactions based on local data as well as to evaluate this mechanism in other regions. In addition, ammonia emissions were found to be a key driving variable of the spatial patterns of sulfate enhancement due to the new pathway. Substantial efforts are needed to improve the accuracy of the ammonia emission inventory.

Increasing emphasis has been placed on characterizing the contributions and the uncertainties of ozone imported from outside the US. In chemical transport models (CTMs), the ozone transported through lateral boundaries (referred to as LB ozone hereafter) undergoes a series of physical and chemical processes in CTMs, which are important sources of the uncertainty in estimating the impact of LB ozone on ozone levels at the surface. By implementing inert tracers for LB ozone, the study seeks to better understand how differing representations of physical processes in regional CTMs may lead to differences in the simulated LB ozone that eventually reaches the surface across the US. For all the simulations in this study (including WRF∕CMAQ, WRF∕CAMx, COSMO-CLM∕CMAQ, and WRF∕DEHM), three chemically inert tracers that generally represent the altitude ranges of the planetary boundary layer (BC1), free troposphere (BC2), and upper troposphere–lower stratosphere (BC3) are tracked to assess the simulated impact of LB specification. Comparing WRF∕CAMx with WRF∕CMAQ, their differences in vertical grid structure explain 10 %–60 % of their seasonally averaged differences in inert tracers at the surface. Vertical turbulent mixing is the primary contributor to the remaining differences in inert tracers across the US in all seasons. Stronger vertical mixing in WRF∕CAMx brings more BC2 downward, leading to higher BCT (BCT=BC1+BC2+BC3) and BC2∕BCT at the surface in WRF∕CAMx. Meanwhile, the differences in inert tracers due to vertical mixing are partially counteracted by their difference in sub-grid cloud mixing over the southeastern US and the Gulf Coast region during summer. The process of dry deposition adds extra gradients to the spatial distribution of the differences in DM8A BCT by 5–10 ppb during winter and summer. COSMO-CLM∕CMAQ and WRF∕CMAQ show similar performance in inert tracers both at the surface and aloft through most seasons, which suggests similarity between the two models at process level. The largest difference is found in summer. Sub-grid cloud mixing plays a primary role in their differences in inert tracers over the southeastern US and the oceans in summer. Our analysis of the vertical profiles of inert tracers also suggests that the model differences in dry deposition over certain regions are offset by the model differences in vertical turbulent mixing, leading to small differences in inert tracers at the surface in these regions.

This study evaluates simulated vertical ozone profiles produced in the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) against ozonesonde observations in North America for the year 2010. Four research groups from the United States (US) and Europe have provided modeled ozone vertical profiles to conduct this analysis. Because some of the modeling systems differ in their meteorological drivers, wind speed and temperature are also included in the analysis. In addition to the seasonal ozone profile evaluation for 2010, we also analyze chemically inert tracers designed to track the influence of lateral boundary conditions on simulated ozone profiles within the modeling domain. Finally, cases of stratospheric ozone intrusions during May–June 2010 are investigated by analyzing ozonesonde measurements and the corresponding model simulations at Intercontinental Chemical Transport Experiment Ozonesonde Network Study (IONS) experiment sites in the western United States. The evaluation of the seasonal ozone profiles reveals that, at a majority of the stations, ozone mixing ratios are underestimated in the 1–6 km range. The seasonal change noted in the errors follows the one seen in the variance of ozone mixing ratios, with the majority of the models exhibiting less variability than the observations. The analysis of chemically inert tracers highlights the importance of lateral boundary conditions up to 250 hPa for the lower-tropospheric ozone mixing ratios (0–2 km). Finally, for the stratospheric intrusions, the models are generally able to reproduce the location and timing of most intrusions but underestimate the magnitude of the maximum mixing ratios in the 2–6 km range and overestimate ozone up to the first kilometer possibly due to marine air influences that are not accurately described by the models. The choice of meteorological driver appears to be a greater predictor of model skill in this altitude range than the choice of air quality model.

In the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3), and as contribution to the second phase of the Hemispheric Transport of Air Pollution (HTAP2) activities for Europe and North America, the impacts of a 20 % decrease of global and regional anthropogenic emissions on surface air pollutant levels in 2010 are simulated by an international community of regional-scale air quality modeling groups, using different state-of-the-art chemistry and transport models (CTMs). The emission perturbations at the global level, as well as over the HTAP2-defined regions of Europe, North America and East Asia, are first simulated by the global Composition Integrated Forecasting System (C-IFS) model from European Centre for Medium-Range Weather Forecasts (ECMWF), which provides boundary conditions to the various regional CTMs participating in AQMEII3. On top of the perturbed boundary conditions, the regional CTMs used the same set of perturbed emissions within the regional domain for the different perturbation scenarios that introduce a 20 % reduction of anthropogenic emissions globally as well as over the HTAP2-defined regions of Europe, North America and East Asia. Results show that the largest impacts over both domains are simulated in response to the global emission perturbation, mainly due to the impact of domestic emission reductions. The responses of NO2, SO2 and PM concentrations to a 20 % anthropogenic emission reduction are almost linear (∼ 20 % decrease) within the global perturbation scenario with, however, large differences in the geographical distribution of the effect. NO2, CO and SO2 levels are strongly affected over the emission hot spots. O3 levels generally decrease in all scenarios by up to ∼ 1 % over Europe, with increases over the hot spot regions, in particular in the Benelux region, by an increase up to ∼ 6 % due to the reduced effect of NOx titration. O3 daily maximum of 8 h running average decreases in all scenarios over Europe, by up to ∼ 1 %. Over the North American domain, the central-to-eastern part and the western coast of the US experience the largest response to emission perturbations. Similar but slightly smaller responses are found when domestic emissions are reduced. The impact of intercontinental transport is relatively small over both domains, however, still noticeable particularly close to the boundaries. The impact is noticeable up to a few percent, for the western parts of the North American domain in response to the emission reductions over East Asia. O3 daily maximum of 8 h running average decreases in all scenarios over north Europe by up to ∼ 5 %. Much larger reductions are calculated over North America compared to Europe. In addition, values of the Response to Extra-Regional Emission Reductions (RERER) metric have been calculated in order to quantify the differences in the strengths of non-local source contributions to different species among the different models. We found large RERER values for O3 (∼ 0.8) over both Europe and North America, indicating a large contribution from non-local sources, while for other pollutants including particles, low RERER values reflect a predominant control by local sources. A distinct seasonal variation in the local vs. non-local contributions has been found for both O3 and PM2.5, particularly reflecting the springtime long-range transport to both continents.

The impact of air pollution on human health and the associated external costs in Europe and the United States (US) for the year 2010 are modeled by a multi-model ensemble of regional models in the frame of the third phase of the Air Quality Modelling Evaluation International Initiative (AQMEII3). The modeled surface concentrations of O3, CO, SO2 and PM2.5 are used as input to the Economic Valuation of Air Pollution (EVA) system to calculate the resulting health impacts and the associated external costs from each individual model. Along with a base case simulation, additional runs were performed introducing 20 % anthropogenic emission reductions both globally and regionally in Europe, North America and east Asia, as defined by the second phase of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP2). Health impacts estimated by using concentration inputs from different chemistry–transport models (CTMs) to the EVA system can vary up to a factor of 3 in Europe (12 models) and the United States (3 models). In Europe, the multi-model mean total number of premature deaths (acute and chronic) is calculated to be 414 000, while in the US, it is estimated to be 160 000, in agreement with previous global and regional studies. The economic valuation of these health impacts is calculated to be EUR 300 billion and 145 billion in Europe and the US, respectively. A subset of models that produce the smallest error compared to the surface observations at each time step against an all-model mean ensemble results in increase of health impacts by up to 30 % in Europe, while in the US, the optimal ensemble mean led to a decrease in the calculated health impacts by ∼ 11 %. A total of 54 000 and 27 500 premature deaths can be avoided by a 20 % reduction of global anthropogenic emissions in Europe and the US, respectively. A 20 % reduction of North American anthropogenic emissions avoids a total of ∼ 1000 premature deaths in Europe and 25 000 total premature deaths in the US. A 20 % decrease of anthropogenic emissions within the European source region avoids a total of 47 000 premature deaths in Europe. Reducing the east Asian anthropogenic emissions by 20 % avoids ∼ 2000 total premature deaths in the US. These results show that the domestic anthropogenic emissions make the largest impacts on premature deaths on a continental scale, while foreign sources make a minor contribution to adverse impacts of air pollution.

The Goddard Earth Observing System global chemical transport (GEOS-Chem) model was used at 2° × 2.5° resolution to simulate ozone formation for a base case representing year 2010 and a natural background case without worldwide anthropogenic emissions. These simulations provided boundary concentrations for base and natural background simulations with the Comprehensive Air Quality Model with Extensions (CAMx) on a North American domain (one-way nested) at 12 km × 12 km resolution over March–September 2010. The predicted maximum daily average 8 h (MDA8) background ozone for the US is largest in the mountainous areas of Colorado, New Mexico, Arizona, and California. The background MDA8 ozone in some of these locations exceeds 60 ppb, when averaged over the 10 days with the largest base-case ozone (T10base average). The background ozone generally becomes both a larger fraction of the base-case ozone in the western US and a smaller fraction in the eastern US when proceeding from spring to summer to the T10base average. The ozone difference between the base and background cases represents the increment to ozone from all anthropogenic sources. The path-integral method was applied to allocate this anthropogenic ozone increment to US anthropogenic emissions, Canadian/Mexican anthropogenic emissions, and the anthropogenic components of the lateral and top boundary concentrations (BCs). Using the T10base average MDA8 ozone, the relative importance of the sources is generally US emissions > anthropogenic lateral BCs > Canadian/Mexican emissions ≫ anthropogenic top BCs. Specifically, for 10 US urban areas, the source contributions were 12–53 ppb for US emissions, 3–9 ppb for lateral BCs, 0.2–3 ppb for Canadian/Mexican emissions, and ≤ 0.1 ppb for top BCs. The contributions of the lateral BCs are largest for the higher-elevation US sites in the Intermountain West and along the western boundary of the domain. In Denver, for example, the lateral BCs contribute 44 % to the T10base anthropogenic increment. Averaging over the 10 days in the background case with the largest MDA8 ozone (T10bkgd), the contribution from US emissions is reduced to 3–13 ppb at the 10 urban sites, leading to a reduction in the anthropogenic ozone increment. The contribution of the Canadian/Mexican emissions remains about the same (0.3–2 ppb), and the contribution from the lateral BCs increases (5–10 ppb), especially at the lower-elevation urban sites. The net effect at the urban sites is that the relative importance of the anthropogenic lateral BCs is significantly greater for the T10bkgd average than the T10base average. This is also true for rural sites studied, where the anthropogenic lateral BC contribution becomes as large as 68 % of the anthropogenic increment for the T10bkgd average. In addition to the source apportionment, we also used surface and ozonesonde measurements to evaluate GEOS-Chem and CAMx performance.

Intercontinental ozone (O3) transport extends the geographic range of O3 air pollution impacts and makes local air pollution management more difficult. Phase 3 of the Air Quality Modeling Evaluation International Initiative (AQMEII-3) is examining the contribution of intercontinental transport to regional air quality by applying regional-scale atmospheric models jointly with global models. We investigate methods for tracing O3 from global models within regional models. The CAMx photochemical grid model was used to track contributions from boundary condition (BC) O3 over a North American modeling domain for calendar year 2010 using a built-in tracer module called RTCMC. RTCMC can track BC contributions using chemically reactive tracers and also using inert tracers in which deposition is the only sink for O3. Lack of O3 destruction chemistry in the inert tracer approach leads to overestimation biases that can exceed 10 ppb. The flexibility of RTCMC also allows tracking O3 contributions made by groups of vertical BC layers. The largest BC contributions to seasonal average daily maximum 8 h averages (MDA8) of O3 over the US are found to be from the mid-troposphere (over 40 ppb) with small contributions (a few ppb) from the upper troposphere–lower stratosphere. Contributions from the lower troposphere are shown to not penetrate very far inland. Higher contributions in the western than the eastern US, reaching an average of 57 ppb in Denver for the 30 days with highest MDA8 O3 in 2010, present a significant challenge to air quality management approaches based solely on local or US-wide emission reductions. The substantial BC contribution to MDA8 O3 in the Intermountain West means regional models are particularly sensitive to any biases and errors in the BCs. A sensitivity simulation with reduced BC O3 in response to 20 % lower emissions in Asia found a near-linear relationship between the BC O3 changes and surface O3 changes in the western US in all seasons and across the US in fall and winter. However, the surface O3 decreases are small: below 1 ppb in spring and below 0.5 ppb in other seasons.

Source apportionment modeling provides valuable information on the contributions of different source sectors and/or source regions to ozone (O3) or fine particulate matter (PM2.5) concentrations. This information can be useful in designing air quality management strategies and in understanding the potential benefits of reducing emissions from a particular source category. The Comprehensive Air quality Model with Extensions (CAMx) offers unique source attribution tools, called the Ozone and Particulate Source Apportionment Technology (OSAT/PSAT), which track source contributions. We present results from a CAMx source attribution modeling study for a summer month and a winter month using a recently evaluated European CAMx modeling database developed for Phase 3 of the Air Quality Model Evaluation International Initiative (AQMEII). The contributions of several source sectors (including model boundary conditions of chemical species representing transport of emissions from outside the modeling domain as well as initial conditions of these species) to O3 or PM2.5 concentrations in Europe were calculated using OSAT and PSAT, respectively. A 1-week spin-up period was used to reduce the influence of initial conditions. Evaluation focused on 16 major cities and on identifying source sectors that contributed above 5 %. Boundary conditions have a large impact on summer and winter ozone in Europe and on summer PM2.5, but they are only a minor contributor to winter PM2.5. Biogenic emissions are important for summer ozone and PM2.5. The important anthropogenic sectors for summer ozone are transportation (both on-road and non-road), energy production and conversion, and industry. In two of the 16 cities, solvent and product also contributed above 5 % to summertime ozone. For summertime PM2.5, the important anthropogenic source sectors are energy, transportation, industry, and agriculture. Residential wood combustion is an important anthropogenic sector in winter for PM2.5 over most of Europe, with larger contributions in central and eastern Europe and the Nordic cities. Other anthropogenic sectors with large contributions to wintertime PM2.5 include energy, transportation, and agriculture.

Through the comparison of several regional-scale chemistry transport modeling systems that simulate meteorology and air quality over the European and North American continents, this study aims at (i) apportioning error to the responsible processes using timescale analysis, (ii) helping to detect causes of model error, and (iii) identifying the processes and temporal scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition, and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2. 5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance, and covariance) can help assess the nature and quality of the error. Each of the error components is analyzed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intraday) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emission and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high interdependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. This will require evaluation methods that are able to frame the impact on error of processes, conditions, and fluxes at the surface. For example, error due to emission and boundary conditions is dominant for primary species (CO, particulate matter (PM)), while errors due to meteorology and chemistry are most relevant to secondary species, such as ozone. Some further aspects emerged whose interpretation requires additional consideration, such as the uniformity of the synoptic error being region- and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

Photochemical grid models (PGMs) are used to simulate tropospheric ozone and quantify its response to emission changes. PGMs are often applied for annual simulations to provide both maximum concentrations for assessing compliance with air quality standards and frequency distributions for assessing human exposure. Efficient methods for computing ozone at different emission levels can improve the quality of ozone air quality management efforts. This study demonstrates the feasibility of using the decoupled direct method (DDM) to calculate first- and second-order sensitivity of ozone to anthropogenic NOx and VOC emissions in annual PGM simulations at continental scale. Algebraic models are developed that use Taylor series to produce complete annual frequency distributions of hourly ozone at any location and any anthropogenic emission level between zero and 100%, adjusted independently for NOx and VOC. We recommend computing the sensitivity coefficients at the midpoint of the emissions range over which they are intended to be applied, in this case with 50% anthropogenic emissions. The algebraic model predictions can be improved by combining sensitivity coefficients computed at 10 and 50% anthropogenic emissions. Compared to brute force simulations, algebraic model predictions tend to be more accurate in summer than winter, at rural than urban locations, and with 100% than zero anthropogenic emissions. Equations developed to combine sensitivity coefficients computed with 10 and 50% anthropogenic emissions are able to reproduce brute force simulation results with zero and 100% anthropogenic emissions with a mean bias of less than 2 ppb and mean error of less than 3 ppb averaged over 22 US cities.

This study is conducted in the framework of the Air Quality Modelling Evaluation International Initiative (AQMEII) and aims at the operational evaluation of an ensemble of 12 regional-scale chemical transport models used to predict air quality over the North American (NA) and European (EU) continents for 2006. The modelled concentrations of ozone and CO, along with the meteorological fields of wind speed (WS) and direction (WD), temperature (T), and relative humidity (RH), are compared against high-quality in-flight measurements collected by instrumented commercial aircraft as part of the Measurements of OZone, water vapour, carbon monoxide and nitrogen oxides by Airbus In-service airCraft (MOZAIC) programme. The evaluation is carried out for five model domains positioned around four major airports in NA (Portland, Philadelphia, Atlanta, and Dallas) and one in Europe (Frankfurt), from the surface to 8.5 km. We compare mean vertical profiles of modelled and measured variables for all airports to compute error and variability statistics, perform analysis of altitudinal error correlation, and examine the seasonal error distribution for ozone, including an estimation of the bias introduced by the lateral boundary conditions (BCs). The results indicate that model performance is highly dependent on the variable, location, season, and height (e.g. surface, planetary boundary layer (PBL) or free troposphere) being analysed. While model performance for T is satisfactory at all sites (correlation coefficient in excess of 0.90 and fractional bias ≤ 0.01 K), WS is not replicated as well within the PBL (exhibiting a positive bias in the first 100 m and also underestimating observed variability), while above 1000 m, the model performance improves (correlation coefficient often above 0.9). The WD at NA airports is found to be biased in the PBL, primarily due to an overestimation of westerly winds. RH is modelled well within the PBL, but in the free troposphere large discrepancies among models are observed, especially in EU. CO mixing ratios show the largest range of modelled-to-observed standard deviations of all the examined species at all heights and for all airports. Correlation coefficients for CO are typically below 0.6 for all sites and heights, and large errors are present at all heights, particularly in the first 250 m. Model performance for ozone in the PBL is generally good, with both bias and error within 20%. Profiles of ozone mixing ratios depend strongly on surface processes, revealed by the sharp gradient in the first 2 km (10 to 20 ppb km−1). Modelled ozone in winter is biased low at all locations in the NA, primarily due to an underestimation of ozone from the BCs. Most of the model error in the PBL is due to surface processes (emissions, transport, photochemistry), while errors originating aloft appear to have relatively limited impact on model performance at the surface. Suggestions for future work include interpretation of the model-to-model variability and common sources of model bias, and linking CO and ozone bias to the bias in the meteorological fields. Based on the results from this study, we suggest possible in-depth, process-oriented and diagnostic investigations to be carried out next.

The heterogeneous hydrolysis of dinitrogen pentoxide (N2O5) has typically been modeled as only producing nitric acid. However, recent field studies have confirmed that the presence of particulate chloride alters the reaction product to produce nitryl chloride (ClNO2) which undergoes photolysis to generate chlorine atoms and nitrogen dioxide (NO2). Both chlorine and NO2 affect atmospheric chemistry and air quality. We present an updated gas-phase chlorine mechanism that can be combined with the Carbon Bond 05 mechanism and incorporate the combined mechanism into the Community Multiscale Air Quality (CMAQ) modeling system. We then update the current model treatment of heterogeneous hydrolysis of N2O5 to include ClNO2 as a product. The model, in combination with a comprehensive inventory of chlorine compounds, reactive nitrogen, particulate matter, and organic compounds, is used to evaluate the impact of the heterogeneous ClNO2 production on air quality across the United States for the months of February and September in 2006. The heterogeneous production increases ClNO2 in coastal as well as many in-land areas in the United States. Particulate chloride derived from sea-salts, anthropogenic sources, and forest fires activates the heterogeneous production of ClNO2. With current estimates of tropospheric emissions, it modestly enhances monthly mean 8-h ozone (up to 1–2 ppbv or 3–4%) but causes large increases (up to 13 ppbv) in isolated episodes. This chemistry also substantially reduces the mean total nitrate by up to 0.8–2.0 μg m−3 or 11–21%. Modeled ClNO2 accounts for up to 6% of the monthly mean total reactive nitrogen. Sensitivity results of the model suggest that heterogeneous production of ClNO2 can further increase O3 and reduce TNO3 if elevated particulate-chloride levels are present in the atmosphere.

Air quality managers in areas exceeding air pollution standards are motivated to understand where there are further opportunities to reduce NO x emissions to improve ozone and PM 2.5 air quality. In this project, we use a combination of aircraft remote sensing (i.e., GCAS), source apportionment models (i.e., CAMx), and regression models to investigate NO x emissions from individual source-sectors in Houston, TX. In prior work, GCAS column NO 2 was shown to be close to the "truth" for validating column NO 2 in model simulations. Column NO 2 from CAMx was substantially low biased compared to Pandora (-20%) and GCAS measurements (-31%), suggesting an underestimate of local NO x emissions. We applied a flux divergence method to the GCAS and CAMx data to distinguish the linear shape of major highways and identify NO 2 underestimates at highway locations. Using a multiple linear regression (MLR) model, we isolated on-road, railyard, and "other" NO x emissions as the likeliest cause of this low bias, and simultaneously identified a potential overestimate of shipping NO x emissions. Based on the MLR, we modified on-road and shipping NO x emissions in a new CAMx simulation and increased the background NO 2 , and better agreement was found with GCAS measurements: bias improved from -31% to -10% and r 2 improved from 0.78 to 0.80. This study outlines how remote sensing data, including fine spatial information from newer geostationary instruments, can be used in concert with chemical transport models to provide actionable information for air quality managers to identify further opportunities to reduce NO x emissions., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Comprehensive Air Quality Model with extensions (CAMx)-Decoupled Direct Method (DDM) simulations of first-order ozone (O 3 ) sensitivity to nitrogen oxides (NO x ) and volatile organic compounds (VOCs) emissions were performed and combined with modelled [Formula: see text] ratios to obtain a range of thresholds for determining O 3 -sensitivity regimes for different areas of China. Utilising the new threshold ranges for photochemical indicators, the method for determining O 3 formation in the Ozone Source Apportionment Technology (OSAT) module within CAMx was improved by a dynamically varied threshold of [Formula: see text] ratio. The O 3 concentration contributions in the newly added transition regime were apportioned to NO x and VOCs emissions in proportion to the relationship between the [Formula: see text] ratio and first-order O 3 sensitivity. The source contributions of O 3 concentrations from different emission sectors from June to September 2019 were compared using the original and improved CAMx-OSAT. The results showed that the O 3 concentration contributions changed significantly in the NO x -limited regime, with a maximum decrease of 21.89%, while the contributions increased by up to 7.57% in the VOC-limited regime, and were within 15 µg/m 3 in the transition regime. The modified OSAT module enabled a more sophisticated attribution of O 3 to precursor emissions and may have far-reaching implications for informing O 3 pollution control policy., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2023. Published by Elsevier B.V.)

Harmful use of illicit drugs and/or alcohol is linked to life-limiting illness and complex health and social care needs, but people who use substances and have complex needs do not receive timely palliative care and fail to achieve quality standards for a good death. They and their families often require support from multiple health and social care services which are shown to be poorly integrated and fail to deliver interdisciplinary care. This study aimed to identify the existing barriers and facilitators within and between services in providing this population with a good death. Using a mixed methods approach of survey, focus groups and semi-structured interviews, we explored the perspectives of practitioner and management staff across a range of health and social disciplines and organisations in one combined authority in a large city in the north west of England. Our findings indicate that practitioners want to provide better care for this client group, but face structural, organisational and professional boundary barriers to delivering integrated and shared care. Differences in philosophy of care, piecemeal commissioning and funding of services, and regulatory frameworks for different services, lead to poor and inequitable access to health and social care services. Ways forward for improving care are suggested as bespoke hostel-based accommodation for palliative care for this client group, and specialist link workers who can transcend professional and organisational boundaries to support co-ordination of services and support. We conclude that it is no longer adequate to call for more training, better communication and improved joint working. Complex care at the end of life requires creative and cohesive systemic responses that enable multi-disciplinary practitioners to provide the care they wish to give and enables individuals using substances to get the respect and quality service they deserve., (© 2024. The Author(s).)

Secondary organic aerosol (SOA) is an important component of atmospheric fine particulate matter (PM 2.5 ), with contributions from anthropogenic and biogenic volatile organic compounds (AVOC and BVOC) and semi- (SVOC) and intermediate volatility organic compounds (IVOC). Policymakers need to know which SOA precursors are important but accurate simulation of SOA magnitude and contributions remain uncertain. Findings from existing SOA modeling studies have many inconsistencies due to differing emission inventory methodologies/assumptions, air quality model (AQM) algorithms, and other aspects of study methodologies. To address some of the inconsistencies, we investigated the role of different AQM SOA algorithms by applying two commonly used models, CAMx and CMAQ, with consistent emission inventories to simulate SOA concentrations and contributions for July and November 2018 in China. Both models have a volatility basis set (VBS) SOA algorithm but with different parameters and treatments of SOA photochemical aging. SOA generated from BVOC (i.e., BSOA) is found to be more important in southern China. In contrast, SOA generated from anthropogenic precursors is more prevalent in the North China Plain (NCP), Yangtze River Delta (YRD), Sichuan Basin and Central China. Both models indicate negligible SOA formation from SVOC emissions compared to other precursors. In July, when BVOC emissions are abundant, SOA is predominantly contributed by BSOA (except for NCP), followed by IVOC-SOA (i.e., SOA produced from IVOC) and ASOA (i.e., SOA produced from anthropogenic VOC). In contrast, in November, IVOC became the leading SOA contributor for all selected regions except PRD, illustrating the important contribution of IVOC emissions to SOA formation. While both models generally agree in terms of the spatial distributions and seasonal variations of different SOA components, CMAQ tends to predict higher BSOA, while CAMx generates higher ASOA concentrations. As a result, CMAQ results suggest that BSOA concentration is always higher than ASOA in November, while CAMx emphasizes the importance of ASOA. Utilizing a conceptual model, we found that different treatment of SOA aging between the two models is a major cause of differences in simulated ASOA concentrations. The step-wise SOA aging scheme implemented in the CAMx VBS (based on gas-phase reactions with OH radical and similar to other models) exhibits a strong enhancement effect on simulated ASOA concentrations, and this effect increases with the ambient organic aerosol (OA) concentrations. The CMAQ aerosol module implements a different SOA aging scheme that represents particle-phase oligomerization and has smaller impacts on total OA. Different structures and/or parameters of the SOA aging schemes are being used in current models, which could greatly affect model simulations of OA in ways that are difficult to anticipate. Our results indicate that future control policies should aim at reducing IVOC emissions as well as traditional VOC emissions. In addition, aging schemes are the major driver in CMAQ vs. CAMx treatments of ASOA and their resulting predicted mass. More sophisticated measurement data (e.g., with resolved OA components) and/or chamber experiments (e.g., investigating how aging influences SOA yields) are needed to better characterize SOA aging and constrain model parameterizations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Adopting electric end-use technologies instead of fossil-fueled alternatives, known as electrification, is an important economy-wide decarbonization strategy that also reduces criteria pollutant emissions and improves air quality. In this study, we evaluate CO 2 and air quality co-benefits of electrification scenarios by linking a detailed energy systems model and a full-form photochemical air quality model in the United States. We find that electrification can substantially lower CO 2 and improve air quality and that decarbonization policy can amplify these trends, which yield immediate and localized benefits. In particular, transport electrification can improve ozone and fine particulate matter (PM 2.5 ), though the magnitude of changes varies regionally. However, growing activity from non-energy-related PM 2.5 sources-such as fugitive dust and agricultural emissions-can offset electrification benefits, suggesting that additional measures beyond CO 2 policy and electrification are needed to meet air quality goals. We illustrate how commonly used marginal emissions approaches systematically underestimate reductions from electrification., (© 2022. The Author(s).)

This discussion article examines narrative positioning related to pain management for people who use substances at the end of life. We explore how dominant narrative genres associated with biomedicine, such as 'restitution' and narratives common within the context of drug services such as 'recovery' can hinder effective pain management within this population. We argue that these discourses can marginalise the ethical self-identity of patients who use substances at the end of life. It can also trouble health and social care professionals in supporting patients and generating counter-narratives that challenge those often associated with substance use. Stigma is a common experience for this population with stereotyping as 'junkies' and associated with criminality. They are positioned as drug-seeking, and this requires more surveillance at the end of life when opioid therapy is potentially more available and authorised. This can make it challenging to generate 'companion' stories that are positive and maintain moral adequacy. Dominant biomedical narrative genres often prevent the recognition of the fractured stories that people using substances can often present with. This can lead to narrative silencing and to the under treatment of pain. The person's self-identity is invested in narratives of recovery, and opioid use symbolises their addicted past because for practitioners, this population is at clinical risk with the potential for drug seeking behaviours. Whilst not requiring formal ethical review this discussion paper was constructed in accordance with good scientific practice with the work of other researchers respected and cited appropriately.

Chemical sensitivity analysis (CSA) is a new probing tool for sampling sensitivities to chemistry parameters during a three-dimensional (3-D) simulation. CSA was applied to rank all of the parameters in the Carbon Bond 6 revision 4 (CB6r4) mechanism and to create an ensemble of six chemical mechanisms representing higher and lower O 3 formations than CB6r4. This ensemble of mechanisms was used to estimate the uncertainty from the chemistry in a 3-D simulation and combined with uncertainties from other model inputs obtained from calculations of their sensitivities. The overall uncertainty (1σ) in O 3 predictions for eastern Texas was 10-11 ppb in the Gulf of Mexico near Galveston and 7-8 ppb in much of the rest of the domain on the higher O 3 days of June 2012. As a percent of the O 3 concentration, the uncertainty was more uniform over the domain, 11-14%. Chemistry and emissions make the largest contributions to the O 3 uncertainty. Uncertainty in the dry deposition velocities is less important in urban areas and the Gulf, but it is similar in importance to the uncertainty in chemistry and emissions at most other locations. Uncertainty in O 3 boundary concentrations is the least important.

The accuracy of atmospheric chemical mechanisms used in air quality models is critical for robustly predicting the production and decay of air pollutants and thus to develop strategies to reduce concentrations that are above levels harmful to humans and ecosystems. In this study we document, evaluate and analyze the implementation of the CB6r3 chemical mechanism used in the Community Multiscale Air Quality (CMAQ) model, including changes that have been to the standard version, and demonstrate the impact of this update on predictions. In general, CB6r3 slightly improves the predictions of ozone and oxides of nitrogen, while providing more consistency with current scientific understanding. Nitric acid is generally overpredicted in both winter and summer, and ongoing work continues to address this overprediction and update other aspects of the mechanism.

Photochemical grid models are addressing an increasing variety of air quality related issues, yet procedures and metrics used to evaluate their performance remain inconsistent. This impacts the ability to place results in quantitative context relative to other models and applications, and to inform the user and affected community of model uncertainties and weaknesses. More consistent evaluations can serve to drive improvements in the modeling process as major weaknesses are identified and addressed. The large number of North American photochemical modeling studies published in the peer-reviewed literature over the past decade affords a rich data set from which to update previously established quantitative performance "benchmarks" for ozone and particulate matter (PM) concentrations. Here we exploit this information to develop new ozone and PM benchmarks (goals and criteria) for three well-established statistical metrics over spatial scales ranging from urban to regional and over temporal scales ranging from episodic to seasonal. We also recommend additional evaluation procedures, statistical metrics, and graphical methods for good practice. While we primarily address modeling and regulatory settings in the United States, these recommendations are relevant to any such applications of state-of-the-science photochemical models. Our primary objective is to promote quantitatively consistent evaluations across different applications, scales, models, model inputs, and configurations. The purpose of benchmarks is to understand how good or poor the results are relative to historical model applications of similar nature and to guide model performance improvements prior to using results for policy assessments. To that end, it also remains critical to evaluate all aspects of the model via diagnostic and dynamic methods. A second objective is to establish a means to assess model performance changes in the future. Statistical metrics and benchmarks need to be revisited periodically as model performance and the characteristics of air quality change in the future., Implications: We address inconsistent procedures and metrics used to evaluate photochemical model performance, recommend a specific set of statistical metrics, and develop updated quantitative performance benchmarks for those metrics. We promote quantitatively consistent evaluations across different applications, scales, models, inputs, and configurations, thereby (1) improving the user's ability to quantitatively place results in context and guide model improvements, and (2) better informing users, regulators, and stakeholders of model uncertainties and weaknesses prior to using results for policy assessments. While we primarily address U.S. modeling and regulatory settings, these recommendations are relevant to any such applications of state-of-the-science photochemical models.

U.S.-wide air quality impacts of electrifying vehicles and off-road equipment are estimated for 2030 using 3-D photochemical air quality model and detailed emissions inventories. Electrification reduces tailpipe emissions and emissions from petroleum refining, transport, and storage, but increases electricity demand. The Electrification Case assumes approximately 17% of light duty and 8% of heavy duty vehicle miles traveled and from 17% to 79% of various off-road equipment types considered good candidates for electrification is powered by electricity. The Electrification Case raises electricity demand by 5% over the 2030 Base Case but nitrogen oxide (NO x ) emissions decrease by 209 thousand tons (3%) overall. Emissions of other criteria pollutants also decrease. Air quality benefits of electrification are modest, mostly less than 1 ppb for ozone and 0.5 μg m -3 for fine particulate matter (PM 2.5 ), but widespread. The largest reductions for ozone and PM occur in urban areas due to lower mobile source emissions. Electrifying off-road equipment yields more benefits than electrifying on-road vehicles. Reduced crude oil imports and associated marine vessel emissions cause additional benefits in port cities. Changes in other gas and PM emissions, as well as impacts on acid and nutrient deposition, are discussed.

Unlabelled: Federal Tier 3 motor vehicle emission and fuel sulfur standards have been promulgated in the United States to help attain air quality standards for ozone and PM2.5 (particulate matter with an aerodynamic diameter <2.5 μm). The authors modeled a standard similar to Tier 3 (a hypothetical nationwide implementation of the California Low Emission Vehicle [LEV] III standards) and prior Tier 2 standards for on-road gasoline-fueled light-duty vehicles (gLDVs) to assess incremental air quality benefits in the United States (U.S.) and the relative contributions of gLDVs and other major source categories to ozone and PM2.5 in 2030. Strengthening Tier 2 to a Tier 3-like (LEV III) standard reduces the summertime monthly mean of daily maximum 8-hr average (MDA8) ozone in the eastern U.S. by up to 1.5 ppb (or 2%) and the maximum MDA8 ozone by up to 3.4 ppb (or 3%). Reducing gasoline sulfur content from 30 to 10 ppm is responsible for up to 0.3 ppb of the improvement in the monthly mean ozone and up to 0.8 ppb of the improvement in maximum ozone. Across four major urban areas-Atlanta, Detroit, Philadelphia, and St. Louis-gLDV contributions range from 5% to 9% and 3% to 6% of the summertime mean MDA8 ozone under Tier 2 and Tier 3, respectively, and from 7% to 11% and 3% to 7% of the maximum MDA8 ozone under Tier 2 and Tier 3, respectively. Monthly mean 24-hr PM2.5 decreases by up to 0.5 μg/m(3) (or 3%) in the eastern U.S. from Tier 2 to Tier 3, with about 0.1 μg/m(3) of the reduction due to the lower gasoline sulfur content. At the four urban areas under the Tier 3 program, gLDV emissions contribute 3.4-5.0% and 1.7-2.4% of the winter and summer mean 24-hr PM2.5, respectively, and 3.8-4.6% and 1.5-2.0% of the mean 24-hr PM2.5 on days with elevated PM2.5 in winter and summer, respectively., Implications: Following U.S. Tier 3 emissions and fuel sulfur standards for gasoline-fueled passenger cars and light trucks, these vehicles are expected to contribute less than 6% of the summertime mean daily maximum 8-hr ozone and less than 7% and 4% of the winter and summer mean 24-hr PM2.5 in the eastern U.S. in 2030. On days with elevated ozone or PM2.5 at four major urban areas, these vehicles contribute less than 7% of ozone and less than 5% of PM2.5, with sources outside North America and U.S. area source emissions constituting some of the main contributors to ozone and PM2.5, respectively.

The anthropogenic increment of a species is the difference in concentration between a base-case simulation with all emissions included and a background simulation without the anthropogenic emissions. The Path-Integral Method (PIM) is a new technique that can determine the contributions of individual anthropogenic sources to this increment. The PIM was applied to a simulation of O3 formation in July 2030 in the U.S., using the Comprehensive Air Quality Model with Extensions and assuming advanced controls on light-duty vehicles (LDVs) and other sources. The PIM determines the source contributions by integrating first-order sensitivity coefficients over a range of emissions, a path, from the background case to the base case. There are many potential paths, with each representing a specific emission-control strategy leading to zero anthropogenic emissions, i.e., controlling all sources together versus controlling some source(s) preferentially are different paths. Three paths were considered, and the O3, formaldehyde, and NO2 anthropogenic increments were apportioned to five source categories. At rural and urban sites in the eastern U.S. and for all three paths, point sources typically have the largest contribution to the O3 and NO2 anthropogenic increments, and either LDVs or area sources, the smallest. Results for formaldehyde are more complex.

On-road vehicle emissions of carbon monoxide (CO), nitrogen oxides (NO(x)), and volatile organic compounds (VOCs) during 1995-2009 in the Atlanta Metropolitan Statistical Area were estimated using the Motor Vehicle Emission Simulator (MOVES) model and data from the National Emissions Inventories and the State of Georgia. Statistically significant downward trends (computed using the nonparametric Theil-Sen method) in annual on-road CO, NO(x), and VOC emissions of 6.1%, 3.3%, and 6.0% per year, respectively, are noted during the 1995-2009 period despite an increase in total vehicle distance traveled. The CO and NO(x) emission trends are correlated with statistically significant downward trends in ambient air concentrations of CO and NO(x) in Atlanta ranging from 8.0% to 11.8% per year and from 5.8% to 8.7% per year, respectively, during similar time periods. Weather-adjusted summertime ozone concentrations in Atlanta exhibited a statistically significant declining trend of 2.3% per year during 2001-2009. Although this trend coexists with the declining trends in on-road NO(x), VOC, and CO emissions, identifying the cause of the downward trend in ozone is complicated by reductions in multiple precursors from different source sectors. Implications: Large reductions in on-road vehicle emissions of CO and NO(x) in Atlanta from the late 1990s to 2009, despite an increase in total vehicle distance traveled, contributed to a significant improvement in air quality through decreases in ambient air concentrations of CO and NO(x) during this time period. Emissions reductions in motor vehicles and other source sectors resulted in these improvements and the observed declining trend in ozone concentrations over the past decade. Although these historical trends cannot be extrapolated to the future because pollutant concentration contributions due to on-road vehicle emissions will likely become an increasingly smaller fraction of the atmospheric total, they provide an indication of the benefits of past control measures.

A suite of mechanistic atmospheric and mercury (Hg) cycling and bioaccumulation models is applied to simulate atmospheric Hg deposition and Hg concentrations in the water column and in fish in a Hg-impaired freshwater lake located in the northeastern United States that receives its Hg loading primarily through deposition. Two future-year scenarios evaluate the long-term response of fish tissue Hg concentrations to reductions in local and nationwide coal-fired electric-generating utility and other Hg emissions and an increase or decrease in global (non-US) Hg emissions. Results indicate that fish tissue Hg concentrations in this ecosystem could require approximately 3 yr to 8 yr to begin to respond to declines in US emissions and deposition with a fish Hg reduction proportional to deposition reduction requiring over 50 yr. Furthermore, recovery could potentially be partially or completely offset by growth in non-US Hg emissions., (© 2014 SETAC.)

Unlabelled: The Community Multiscale Air Quality (CMAQ) modeling system Version 5.0 (CMAQv5.0) was released by the US. Environmental Protection Agency (EPA) in February 2012, with an interim release (v5.01) in July 2012. Because CMAQ is a community model, the EPA encourages the development of proven alternative science treatments by external scientists and developers that can be incorporated as part of an official CMAQ release. This paper describes the implementation, evaluation, and testing of a plume-in-grid (PinG) module in CMAQ 5.01. The PinG module, also referred to as Advanced Plume Treatment (APT), provides the capability of resolving sub-grid-scale processes, such as the transport and chemistry of point-source plumes, in a grid model. The new PinG module in CMAQ 5.01 is applied and evaluated for two 15-day summer and winter periods in 2005 to the eastern United States, and the results are compared with those from the base CMAQ 5.01. Eighteen large point sources of NO(x) in the eastern United States were selected for explicit plume treatment with APT in the PinG simulation. The results show that overall model performance is negligibly affected when PinG treatment is included. However the PinG model predicts significantly different contributions of the 18 sources to pollutant concentrations and deposition downwind of the point sources compared to the base model., Implications: This study describes the incorporation of a plume-in-grid (PinG) capability within the latest version of the EPA grid model, CMAQ. The capability addresses the inherent limitation of the grid model to resolve processes, such as the evolution of point-source plumes, which occur at scales much smaller than the grid resolution. The base grid model and the PinG version predict different source contributions to ozone and PM2.5 concentrations that need to be considered when source attribution studies are conducted to determine the impacts of large point sources on downwind concentrations and deposition of primary and secondary pollutants.

Policy Relevant Background (PRB) ozone concentrations are defined by the United States (U.S.) Environmental Protection Agency (EPA) as those concentrations that would occur in the U.S. in the absence of anthropogenic emissions in continental North America (i.e., the U.S, Canada, and Mexico). Estimates of PRB ozone have had an important role historically in the EPA's human health and welfare risk analyses used in establishing National Ambient Air Quality Standards (NAAQS). The margin of safety for the protection of public health in the ozone rulemaking process has been established from human health risks calculated based on PRB ozone estimates. Sensitivity analyses conducted by the EPA have illustrated that changing estimates of PRB ozone concentrations have a progressively greater impact on estimates of mortality risk as more stringent standards are considered. As defined by the EPA, PRB ozone is a model construct, but it is informed by measurements at relatively remote monitoring sites (RRMS). This review examines the current understanding of PRB ozone, based on both model predictions and measurements at RRMS, and provides recommendations for improving the definition and determination of PRB ozone.

The Haynesville Shale is a subsurface rock formation located beneath the Northeast Texas/Northwest Louisiana border near Shreveport. This formation is estimated to contain very large recoverable reserves of natural gas, and during the two years since the drilling of the first highly productive wells in 2008, has been the focus of intensive leasing and exploration activity. The development of natural gas resources within the Haynesville Shale is likely to be economically important but may also generate significant emissions of ozone precursors. Using well production data from state regulatory agencies and a review of the available literature, projections of future year Haynesville Shale natural gas production were derived for 2009-2020 for three scenarios corresponding to limited, moderate, and aggressive development. These production estimates were then used to develop an emission inventory for each of the three scenarios. Photochemical modeling of the year 2012 showed increases in 2012 8-h ozone design values of up to 5 ppb within Northeast Texas and Northwest Louisiana resulting from development in the Haynesville Shale. Ozone increases due to Haynesville Shale emissions can affect regions outside Northeast Texas and Northwest Louisiana due to ozone transport. This study evaluates only near-term ozone impacts, but the emission inventory projections indicate that Haynesville emissions may be expected to increase through 2020.

A doubly resonant cavity was used to search for nonlinear radiofrequency (RF) energy conversion in a range of biological preparations, thereby testing the hypothesis that living tissue can demodulate RF carriers and generate baseband signals. The samples comprised high-density cell suspensions (human lymphocytes and mouse bone marrow cells); adherent cells (IMR-32 human neuroblastoma, G361 human melanoma, HF-19 human fibroblasts, N2a murine neuroblastoma (differentiated and non-differentiated) and Chinese hamster ovary (CHO) cells) and thin sections or slices of mouse tissues (brain, kidney, muscle, liver, spleen, testis, heart and diaphragm). Viable and non-viable (heat killed or metabolically impaired) samples were tested. Over 500 cell and tissue samples were placed within the cavity, exposed to continuous wave (CW) fields at the resonant frequency (f) of the loaded cavity (near 883 MHz) using input powers of 0.1 or 1 mW, and monitored for second harmonic generation by inspection of the output at 2f. Unwanted signals were minimised using low pass filters (≤ 1 GHz) at the input to, and high pass filters (≥ 1 GHz) at the output from, the cavity. A tuned low noise amplifier allowed detection of second harmonic signals above a noise floor as low as -169 dBm. No consistent second harmonic of the incident CW signals was detected. Therefore, these results do not support the hypothesis that living cells can demodulate RF energy, since second harmonic generation is the necessary and sufficient condition for demodulation.