Critical load exceedances for North America and Europe using an ensemble of models and an investigation of causes of environmental impact estimate variability: an AQMEII4 study

Exceedances of critical loads for deposition of sulfur (S) and nitrogen (N) in different ecosystems were estimated using European and North American ensembles of air quality models, under the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4), to identify where the risk of ecosystem harm is expected to occur based on model deposition estimates. The ensembles were driven by common emissions and lateral boundary condition inputs. Model output was regridded to common North American and European 0.125° resolution domains, which were then used to calculate critical load exceedances. Targeted deposition diagnostics implemented in AQMEII4 allowed for an unprecedented level of post-simulation analysis to be carried out and facilitated the identification of specific causes of model-to-model variability in critical load exceedance estimates. Datasets for North American critical loads for acidity for forest soil water and aquatic ecosystems were created for this analysis. These were combined with the ensemble deposition predictions to show a substantial decrease in the area and number of locations in exceedance between 2010 and 2016 (forest soils: 13.2% to 6.1 %; aquatic ecosystems: 21.2% to 11.4 %). All models agreed regarding the direction of the ensemble exceedance change between 2010 and 2016. The North American ensemble also predicted a decrease in both the severity and total area in exceedance between the years 2010 and 2016 for eutrophication-impacted ecosystems in the USA (sensitive epiphytic lichen: 81.5% to 75.8 %). The exceedances for herbaceous-community richness also decreased between 2010 and 2016, from 13.9% to 3.9 %. The uncertainty associated with the North American eutrophication results is high; there were sharp differences between the models in predictions of both total N deposition and the change in N deposition and hence in the predicted eutrophication exceedances between the 2 years. The European ensemble was used to predict relatively static exceedances of critical loads with respect to acidification (4.48% to 4.32% from 2009 to 2010), while eutrophication exceedance increased slightly (60.2% to 62.2 %). While most models showed the same changes in critical load exceedances as the ensemble between the 2 years, the spatial extent and magnitude of exceedances varied significantly between the models. The reasons for this variation were examined in detail by first ranking the relative contribution of different sources of sulfur and nitrogen deposition in terms of deposited mass and model-to-model variability in that deposited mass, followed by their analysis using AQMEII4 diagnostics, along with evaluation of the most recent literature. All models in both the North American and European ensembles had net annual negative biases with respect to the observed wet deposition of sulfate, nitrate, and ammonium. Diagnostics and recent literature suggest that this bias may stem from insufficient cloud scavenging of aerosols and gases and may be improved through the incorporation of multiphase hydrometeor scavenging within the modelling frameworks. The inability of North American models to predict the timing of the seasonal peak in wet ammonium ion deposition (observed maximum was in April, while all models predicted a June maximum) may also relate to the need for multiphase hydrometeor scavenging (absence of snow scavenging in all models employed here). High variability in the relative importance of particulate sulfate, nitrate, and ammonium deposition fluxes between models was linked to the use of updated particle dry-deposition parameterizations in some models. However, recent literature and the further development of some of the models within the ensemble suggest these particulate biases may also be ameliorated via the incorporation of multiphase hydrometeor scavenging. Annual sulfur and nitrogen deposition prediction variability was linked to SO2 and HNO3 dry-deposition parameterizations, and diagnostic analysis showed that the cuticle and soil deposition pathways dominate the deposition mass flux of these species. Further work improving parameterizations for these deposition pathways should reduce variability in model acidifying-gas deposition estimates. The absence of base cation chemistry in some models was shown to be a major factor in positive biases in fine-mode particulate ammonium and particle nitrate concentrations. Models employing ammonia bidirectional fluxes had both the largest- and the smallest-magnitude biases, depending on the model and bidirectional flux algorithm employed. A careful analysis of bidirectional flux models suggests that those with poor NH3 performance may underestimate the extent of NH3 emission fluxes from forested areas. Model-measurement fusion in the form of a simple bias correction was applied to the 2016 critical loads. This generally reduced variability between models. However, the bias correction exercise illustrated the need for observations which close the sulfur and nitrogen budgets in carrying out model-measurement fusion. Chemical transformations between different forms of sulfur and nitrogen in the atmosphere sometimes result in compensating biases in the resulting total sulfur and nitrogen deposition flux fields. If model-measurement fusion is only applied to some but not all of the fields contributing to the total deposition of sulfur or nitrogen, the corrections may result in greater variability between models or less accurate results for an ensemble of models, for those cases where an unobserved or unused observed component contributes significantly to predicted total deposition. Based on these results, an increased process-research focus is therefore recommended for the following model processes and for observations which may assist in model evaluation and improvement: multiphase hydrometeor scavenging combined with updated particle dry-deposition, cuticle, and soil deposition pathway algorithms for acidifying gases, base cation chemistry and emissions, and NH3 bidirectional fluxes. Comparisons with satellite observations suggest that oceanic NH3 emission sources should be included in regional chemical transport models. The choice of a land use database employed within any given model was shown to significantly influence deposition totals in several instances, and employing a common land use database across chemical transport models and critical load calculations is recommended for future work.

Attribution of fine particulate matter and ozone health impacts in Canada to domestic and US emission sources

Exposure to ambient air pollution is associated with a wide range of adverse health effects such as respiratory symptoms, cardiovascular events, and premature mortality. Canada and the United States (US) have worked collaboratively for decades to address transboundary air pollution and its impacts across their shared border. To inform transboundary air quality considerations, we conducted modelling to attribute health impacts from ambient PM2.5 and O3 exposure in Canada to Canadian and US emission sources. We employed emissions, chemical transport, and health impacts modelling for 2015, 2025, and 2035 using a brute-force modelling approach whereby anthropogenic domestic and US emissions were reduced separately by 20 % or 100 %, and the resulting changes in health impacts were estimated across Canada. We find that transboundary PM2.5 and O3 related health impacts vary widely by region, with >80 % of impacts occurring in Central Canada, and most health impacts occurring within 200-300 km of the Canada-US border. The relative contribution of US sources to O3 in Canada is larger than for PM2.5, yet we find that the health impacts from transboundary PM2.5 exceeded those from transboundary O3. Nationally, we estimate that roughly one in five PM2.5 deaths in Canada is attributable to US sources (2000 deaths in 2015) and more than one in two O3 deaths are attributable to US sources (roughly 800 to 1200 deaths in 2015). We project health impacts from domestic and US sources to increase from 2025 to 2035 in Canada. Our results suggest that there are substantial benefits to be gained by domestic and international strategies to reduce PM2.5 in the Canada-US transboundary region.

Parameterization of below-cloud scavenging for polydisperse fine mode aerosols as a function of rain intensity

The below-cloud aerosol scavenging process by precipitation is one of the most important mechanisms to remove aerosols from the atmosphere. Due to its complexity and dependence on both aerosol and raindrop sizes, wet scavenging process has been poorly treated, especially during the removal of fine particles. This makes the numerical simulation of below-cloud scavenging in large-scale aerosol models unrealistic. To consider the slip effects of submicron particles, a simplified expression for the diffusion scavenging was developed by approximating the Cunningham slip correction factor. The derived analytic solution was parameterized as a simple power function of rain intensity under the assumption of the lognormal size distribution of particles. The resultant approximated expression was compared to the observed data and the results of previous studies including a 3D atmospheric chemical transport model simulation. Compared with the default GEOS-Chem coefficient of 0.00106R^0.61 and the observation-based coefficient of 0.0144R^0.9268, the coefficient of a and b in Λ_m = aR^b spread in the range of 0.0002- 0.1959 for a and 0.3261- 0.525 for b over a size distribution of GSD of 1.3-2.5 and a geometric mean diameter of 0.01- 2.5 µm. Overall, this study showed that the scavenging coefficient varies widely by orders of magnitude according to the size distribution of particles and rain intensity. This study also demonstrated that the obtained simplified expression could consider the theoretical approach of aerosol polydispersity. Our proposed analytic approach showed that results can be effectively applied for reduced computational burden in atmospheric modeling.

Development of GRAPES-CUACE adjoint model version 2.0 and its application in sensitivity analysis of ozone pollution in north China

We presented the development of the gaseous chemistry adjoint module of the meteorological-chemical model system GRAPES-CUACE (Global/Regional Assimilation and PrEdiction System coupled with CMA Unified Atmospheric Chemistry Environmental Forecasting System) on the basis of the previously constructed aerosol adjoint module. The latest version of the GRAPES-CUACE adjoint model mainly includes the adjoint of the physical and chemical processes, the adjoint of the transport processes, and the adjoint of interface programs, of both gas and aerosol. The adjoint implementation was validated for the full model, and adjoint results showed good agreement with brute force sensitivities. We also applied the newly developed adjoint model to the sensitivity analysis of an ozone episode occurred in Beijing on July 2, 2017, as well as the design of emission-reduction strategies for this episode. The relationships between the ozone concentration and precursor emissions were well captured by the adjoint model. It is indicated that for a case used here, the Beijing peak ozone concentration was influenced mostly by local emissions (6.2-24.3%), as well as by surrounding emissions, including Hebei (4.4-16.8%), Tianjin (1.8-6.6%), Shandong (1.8-2.6%), and Shanxi (<1%). In addition, reduction of NO_x, VOCs, and CO emissions in these regions would effectively decrease the Beijing peak ozone concentration. This study highlights the capability of GRAPES-CUACE adjoint model in quantifying "emission-concentration" relationship and in providing guidance for environmental control policy.

Investigation of the influence of mineral dust on airborne particulate matter during the COVID-19 epidemic in spring 2020 over China

A regional air quality model system (RAQMS) driven by the Weather Research and Forecasting model (WRF) is applied to investigate the distribution and evolution of mineral dust and anthropogenic aerosols over China in April 2020, when air quality was improved due to reduced human activity during the COVID-19 epidemic, whereas dust storms began to attack China and deteriorated air quality. A dust deflation model was developed and improved mineral dust prediction. Model validation demonstrated that RAQMS was able to reproduce PM₁₀, PM_2.5 and aerosol components reasonably well. China suffered from three dust events in April 2020, with the maximum hourly PM₁₀ concentrations exceeding 700 μg m^-3 in downwind cities over the North China Plain (NCP). Mineral dust dominated PM₁₀ mass (>80%) over the Gobi deserts in north and west China, while it comprised approximately 30-50% of PM₁₀ over wide areas of east China. The domain and monthly mean dust mass fractions in PM₁₀ were estimated to be 47% and 43% over the North China Plain and east China, respectively. On average, mineral dust contributed up to 22% and 21% of PM_2.5 mass over the North China Plain and east China in April 2020, respectively. Sulfate and nitrate produced by heterogeneous chemical reactions on dust surface accounted for approximately 9% and 13% of secondary inorganic aerosols (SIA) concentration over the North China Plain and east China, respectively. The results from this study demonstrated that mineral dust made an important contribution to particulate matter mass during the COVID-19 epidemic in spring 2020 over China.

Effective Radiative Forcings Due To Anthropogenic Emission Changes Under Covid-19 and Post-Pandemic Recovery Scenarios

With the continuation of the Coronavirus Disease 2019 (Covid-19) pandemic, the impacts of this catastrophe on anthropogenic emissions are no longer limited to its early stage. This study quantitatively estimates effective radiative forcings (ERFs) due to anthropogenic well-mixed greenhouse gases (WMGHGs) and aerosols for the period 2020-2050 under the three latest Covid-19 economic-recovery scenarios using an aerosol-climate model. The results indicate that reductions in both WMGHG and aerosol emissions under the Covid-19 green recoveries lead to increases ranging from 0 to 0.3 W m^-2 in global annual mean anthropogenic ERF over the period 2020-2050 relative to the Shared Socioeconomic Pathway 2-4.5 scenario (the baseline case). These positive ERFs are mainly attributed to the rapid and dramatic decreases in atmospheric aerosol content that increase net shortwave radiative flux at the top of atmosphere via weakening the direct aerosol effect and low cloud cover. At the regional scale, reductions in aerosols contribute to positive ERFs throughout the Northern Hemisphere, while the decreased WMGHGs dominate negative ERFs over the areas away from aerosol pollution, such as the Southern Hemisphere oceans. This drives a strong interhemispheric contrast of ERFs. In contrast, the increased anthropogenic emissions under the fossil-fueled recovery scenario lead to an increase of 0.3 W m^-2 in global annual mean ERF in 2050 compared with the baseline case, primarily due to the contribution of WMGHG ERFs. The regional ERF changes are highly dependent on local cloud radiative effects.

Development and application of an automated air quality forecasting system based on machine learning

As one of the most concerned issues in modern society, air quality has received extensive attentions from the public and the government, which promotes the continuous development and progress of air quality forecasting technology. In this study, an automated air quality forecasting system based on machine learning has been developed and applied for daily forecasts of six common pollutants (PM_2.5, PM_10, SO₂, NO₂, O₃, and CO) and pollution levels, which can automatically find the best "Model + Hyperparameters" without human intervention. Five machine learning models and an ensemble model (Stacked Generalization) were integrated into the system, supported by a knowledge base containing the meteorological observed data, pollutant concentrations, pollutant emissions, and model reanalysis data. Then five-year data (2015-2019) of Beijing, Shanghai, Guangzhou, Chengdu, Xi'an, Wuhan, and Changchun in China, were used as an application case to study the effectiveness of the automated forecasting system. Based on the analysis of seven evaluation criteria and pollution level forecasts, combined with the forecasting results for the next 3-days, it is found that the automated system can achieve satisfactory forecasting performance, better than most of numerical model results. This implied that the developed system unveils a good application prospect in the field of environmental meteorology.

A new parameterization of uptake coefficients for heterogeneous reactions on multi-component atmospheric aerosols

Based on laboratory studies and field observations, a new parameterization of uptake coefficients for heterogeneous reactions on multi-component aerosols is developed in this work. The equivalent ratio (ER) of inorganic aerosol is used to establish the quantitative relationship between the heterogeneous uptake coefficients and the composition of aerosols. Incorporating the new ER-dependent scheme, the WRF-CUACE model has been applied to simulate sulfate mass concentrations during December 2017 in the Beijing-Tianjin-Hebei region and evaluate the role of aerosol chemical components played in the sulfate formation. Simulated temporal variations and magnitudes of sulfate show good agreement with the observations by using this new scheme. From clean to polluted cases, although both dominant cations and anions increase significantly, the equivalent ratio decreases gradually and is closer to unity, representing the variation of aerosol compositions, which inhibits the heterogeneous uptake of SO₂, with the uptake coefficient decreasing from 1 × 10^-4 to 5.3 × 10^-5. Based on this phenomenon, a self-limitation process for heterogeneous reactions with the increasing secondary inorganic aerosol from clean to polluted cases is proposed.

Development and Evaluation of Chemistry-Aerosol-Climate Model CAM5-Chem-MAM7-MOSAIC: Global Atmospheric Distribution and Radiative Effects of Nitrate Aerosol

An advanced aerosol treatment, with a focus on semivolatile nitrate formation, is introduced into the Community Atmosphere Model version 5 with interactive chemistry (CAM5-chem) by coupling the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) with the 7-mode Modal Aerosol Module (MAM7). An important feature of MOSAIC is dynamic partitioning of all condensable gases to the different fine and coarse mode aerosols, as governed by mode-resolved thermodynamics and heterogeneous chemical reactions. Applied in the free-running mode from 1995 to 2005 with prescribed historical climatological conditions, the model simulates global distributions of sulfate, nitrate, and ammonium in good agreement with observations and previous studies. Inclusion of nitrate resulted in ∼10% higher global average accumulation mode number concentrations, indicating enhanced growth of Aitken mode aerosols from nitrate formation. While the simulated accumulation mode nitrate burdens are high over the anthropogenic source regions, the sea-salt and dust modes respectively constitute about 74% and 17% of the annual global average nitrate burden. Regional clear-sky shortwave radiative cooling of up to -5 W m-2 due to nitrate is seen, with a much smaller global average cooling of -0.05 W m-2. Significant enhancements in regional cloud condensation nuclei (at 0.1% supersaturation) and cloud droplet number concentrations are also attributed to nitrate, causing an additional global average shortwave cooling of -0.8 W m-2. Taking into consideration of changes in both longwave and shortwave radiation under all-sky conditions, the net change in the top of the atmosphere radiative fluxes induced by including nitrate aerosol is -0.7 W m-2.

Isolating the impact of COVID-19 lockdown measures on urban air quality in Canada

We have investigated the impact of reduced emissions due to COVID-19 lockdown measures in spring 2020 on air quality in Canada's four largest cities: Toronto, Montreal, Vancouver, and Calgary. Observed daily concentrations of NO₂, PM_2.5, and O₃ during a "pre-lockdown" period (15 February-14 March 2020) and a "lockdown" period (22 March-2 May 2020), when lockdown measures were in full force everywhere in Canada, were compared to the same periods in the previous decade (2010-2019). Higher-than-usual seasonal declines in mean daily NO₂ were observed for the pre-lockdown to lockdown periods in 2020. For PM_2.5, Montreal was the only city with a higher-than-usual seasonal decline, whereas for O₃ all four cities remained within the previous decadal range. In order to isolate the impact of lockdown-related emission changes from other factors such as seasonal changes in meteorology and emissions and meteorological variability, two emission scenarios were performed with the GEM-MACH air quality model. The first was a Business-As-Usual (BAU) scenario with baseline emissions and the second was a more realistic simulation with estimated COVID-19 lockdown emissions. NO₂ surface concentrations for the COVID-19 emission scenario decreased by 31 to 34% on average relative to the BAU scenario in the four metropolitan areas. Lower decreases ranging from 6 to 17% were predicted for PM_2.5. O₃ surface concentrations, on the other hand, showed increases up to a maximum of 21% close to city centers versus slight decreases over the suburbs, but O_x (odd oxygen), like NO₂ and PM_2.5, decreased as expected over these cities.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-021-01039-1.

Simulation and Analyses of the Potential Impacts of Different Particle-Size Dust Aerosols Caused by the Qinghai-Tibet Plateau Desertification on East Asia

In this paper on the analysis of the vertical distribution of different-diameter dust aerosols and the potential impacts on East Asia, the sensitivity simulation tests of dust aerosols during 2002–03 were conducted by changing the underlying surface on the Qinghai-Tibet Plateau in the global atmospheric circulation model Community Atmosphere Model (CAM) 3.1. The results show that dust aerosol particles in East Asia are mainly distributed in the diameters of 0.64–5.12 μm. The high concentrations of dust aerosols are centered on the surface in the source areas and gradually raised during the eastward transport across East Asia, reaching a height of 4 km at 120° E. The small dust particles with diameters less than 1.28 μm are transported higher and farther driven by the midlatitude westerlies. The Qinghai-Tibet Plateau desertification leads to increasing concentrations of dust aerosols in all size bins and raisesthe transport height of dust aerosols in East Asia. The long-range transport in the East Asian troposphere is dominated by dust aerosols particles of diameters 0.64–2.56 μm, as well as a large contribution of dust aerosols with diameters larger than 1.28 μm.

A Review of the Representation of Aerosol Mixing State in Atmospheric Models

Aerosol mixing state significantly affects concentrations of cloud condensation nuclei (CCN), wet removal rates, thermodynamic properties, heterogeneous chemistry, and aerosol optical properties, with implications for human health and climate. Over the last two decades, significant research effort has gone into finding computationally-efficient methods for representing the most important aspects of aerosol mixing state in air pollution, weather prediction, and climate models. In this review, we summarize the interactions between mixing-state and aerosol hygroscopicity, optical properties, equilibrium thermodynamics and heterogeneous chemistry. We focus on the effects of simplified assumptions of aerosol mixing state on CCN concentrations, wet deposition, and aerosol absorption. We also summarize previous approaches for representing aerosol mixing state in atmospheric models, and we make recommendations regarding the representation of aerosol mixing state in future modelling studies.

Laboratory Studies of the Cloud Droplet Activation Properties and Corresponding Chemistry of Saline Playa Dust

Playas emit large quantities of dust that can facilitate the activation of cloud droplets. Despite the potential importance of playa dusts for cloud formation, most climate models assume that all dust is nonhygroscopic; however, measurements are needed to clarify the role of dusts in aerosol-cloud interactions. Here, we report measurements of CCN activation from playa dusts and parameterize these results in terms of both κ-Köhler theory and adsorption activation theory for inclusion in atmospheric models. κ ranged from 0.002 ± 0.001 to 0.818 ± 0.094, whereas Frankel-Halsey-Hill (FHH) adsorption parameters of A_FHH = 2.20 ± 0.60 and B_FHH = 1.24 ± 0.14 described the water uptake properties of the dusts. Measurements made using aerosol time-of-flight mass spectrometry (ATOFMS) revealed the presence of halite, sodium sulfates, and sodium carbonates that were strongly correlated with κ underscoring the role that mineralogy, including salts, plays in water uptake by dust. Predictions of κ made using bulk chemical techniques generally showed good agreement with measured values. However, several samples were poorly predicted suggesting that chemical heterogeneities as a function of size or chemically distinct particle surfaces can determine the hygroscopicity of playa dusts. Our results further demonstrate the importance of dust in aerosol-cloud interactions.

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season

Environment and Climate Change Canada's FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2-July 15, and August 15-31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of -7.3 µg m(-3) and 3.1 µg m(-3)), it showed better forecast skill than the RAQDPS (MB of -11.7 µg m(-3) and -5.8 µg m(-3)) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m(-3) also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR).

IMPLICATIONS

Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders.

Aerosols and environmental pollution

The number of publications on atmospheric aerosols has dramatically increased in recent years. This review, predominantly from a European perspective, summarizes the current state of knowledge of the role played by aerosols in environmental pollution and, in addition, highlights gaps in our current knowledge. Aerosol particles are ubiquitous in the Earth's atmosphere and are central to many environmental issues; ranging from the Earth's radiative budget to human health. Aerosol size distribution and chemical composition are crucial parameters that determine their dynamics in the atmosphere. Sources of aerosols are both anthropogenic and natural ranging from vehicular emissions to dust resuspension. Ambient concentrations of aerosols are elevated in urban areas with lower values at rural sites. A comprehensive understanding of aerosol ambient characteristics requires a combination of measurements and modeling tools. Legislation for ambient aerosols has been introduced at national and international levels aiming to protect human health and the environment.