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Further long-term investments in high-quality, research-driven, fit-for-purpose observations of atmospheric composition are needed globally to meet urgent societal needs related to weather, climate, air quality, and other environmental issues. Challenges include maintaining current observing systems in the face of eroding budgets for long-term monitoring and filling the geographical gaps for key constituents needed for sound services and policies. The observing systems can be bolstered through science-for-services applications, by embracing interoperable observation systems and standardized metadata, and ensuring that the data are findable, accessible, interoperable, and reusable. There is an urgent need to move from opportunities-driven one-component observations to more systematic, planned multifunctional infrastructure, where the observational data flow is coupled with Earth system models to serve both operational and research purposes. This approach fosters a community where user experience feeds back into the research components and where mature research results are translated into operational applications. This will lead to faster exploration and exploitation of atmospheric composition information and more impactful applications for science and society. We discuss here the urgent need to (i) achieve global coverage, (ii) harmonize infrastructure operations, (iii) establish focused policies, and (iv) strengthen coordination of atmospheric composition infrastructure. [ABSTRACT FROM AUTHOR]

Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land initial conditions directly downscaled from a coarser resolution dataset led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7) surface and near-surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface air temperature by ∼2 °C. We also show that the LIS land initialization can modify surface air temperature errors almost 10 times as effectively as applying a different atmospheric initialization method. The LIS-NUWRFbased isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20% lower than those computed using the coarser resolution data-initialized NUWRF run, and are closer to aircraft-observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified discrepancies with aircraft-observation-derived emissions on small scales. This is possibly a result of some limitations of MEGAN's parameterization and uncertainty in its inputs on small scales, as well as the representation error and the neglect of horizontal transport in deriving emissions from aircraft data. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling. We anticipate it to also be critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive. [ABSTRACT FROM AUTHOR]

Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that initializing the Noah land surface model directly using a coarser resolution dataset North American Regional Reanalysis (NARR) led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7)'s (near-) surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing the land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF's surface air temperature fields by ~ 2 °C. We also show that the LIS land initialization can modify the surface air temperature errors almost ten times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the NARR-initialized NUWRF run, and are closer to the aircraft observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified errors on small scales, possibly resulted from some limitations of MEGAN's parameterization and its inputs' uncertainty. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling, which we anticipate to be also critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive. [ABSTRACT FROM AUTHOR]

A new parameterization method for anthropogenic heat (AH) parameterization (called NewLUCY) is developed in the WRF-Chem model, which estimates hourly heat fluxes with a single-peak diurnal variation pattern and utilizes updated urban built-up land use data. The impacts of accounting for anthropogenic heat (AH) fluxes on the meteorology and air quality of the Greater Beijing area are studied using this upgraded WRF-Chem model system. Including AH is shown to increase the surface temperature by 0.8°C in daytime and 1.2 °C at nighttime. The Planetary Boundary Layer (PBL) heights are also increased, with a maximum incrementation exceeding 320 m during daytime and 160 m at night. Spatial and vertical distributions of the simulated pollutants are also impacted by the AH. Surface ozone concentrations increase in the urban areas (4ppb for daytime and 18 ppb for nighttime) when AH is included in the analyses. Moreover, the impacts of AH are not limited to the urban centers, but extend regionally. For example, the simulated PM2.5 concentrations increase in the rural areas as well, due to a decrease in rural precipitation rates when AH is included. In general, incorporation of AH increases the accuracy of the predictions comparing to the observations. At the Peking University site (PKU), the mean error (ME) of the 2-m temperature prediction is reduced from 1.55 °C to 0.61 °C. The predictions of the high ozone episodes are also improved. [ABSTRACT FROM AUTHOR]

Abstract: Urbanization in China accompanies economic development and population growth. Changes in land use leads to changes in both meteorological and chemical fields. In this study, the impact of land use change in Jing-Jin-Ji (JJJ, indicating Beijing–Tianjin–Hebei) and Yangtze River Delta (YRD) areas on meteorology and ozone concentrations are studied using the WRF–Chem model. Land use change is represented by two different land cover data sets: USGS and MODIS. Monthly-average simulations show that urbanization causes an increase in 2-m temperature by maximum 2.4 °C and 3.2 °C in these two areas, respectively. Wind speed simulations suggest a decrease (average 1.2 m s−1) in daytime for YRD, and in nighttime for JJJ. Dew point differences show a dry effect over both areas, with maximum −3 °C in JJJ and −2.4 °C in YRD. Planetary Boundary Layer (PBL) height increases by 400 m (maximum in JJJ) and 600 m (maximum in YRD) for daytime, and nighttime increases are less than 100 m. Daytime ozone concentrations in JJJ increase by 20 ppb due to urbanization, while in YRD the difference is around 5 ppb. Compared to observations, mean errors in urban areas was improved when using updated land use information by 14.2% and 35.6%, and in suburban areas by 5.8% and 10.7%. Updating land use data set in air quality modeling is important in application to regions with rapid urbanization such as China. The effects due to land use change can be as large as those due to 20% increase in emissions. [Copyright &y& Elsevier]

Limitations in current capabilities to constrain aerosols adversely impact atmospheric simulations. Typically, aerosol burdens within models are constrained employing satellite aerosol optical properties, which are not available under cloudy conditions. Here we set the first steps to overcome the long-standing limitation that aerosols cannot be constrained using satellite remote sensing under cloudy conditions. We introduce a unique data assimilation method that uses cloud droplet number (Wd) retrievals to improve predicted below-cloud aerosol mass and number concentrations. The assimilation, which uses an adjoint aerosol activation parameterization, improves agreement with independent Nd observations and with in situ aerosol measurements below shallow cumulus clouds. The impacts of a single assimilation on aerosol and cloud forecasts extend beyond 24 h. Unlike previous methods, this technique can directly improve predictions of near-surface fine mode aerosols responsible for human health impacts and low-cloud radiative forcing. Better constrained aerosol distributions will help improve health effects studies, atmospheric emissions estimates, and airquality, weather, and climate predictions. [ABSTRACT FROM AUTHOR]

Abstract: The sectoral and regional contributions from northern hemisphere anthropogenic and biomass burning emission sectors to black carbon (BC) distributions over the continental United States (CONUS) in summer 2008 are studied using the Sulfur Transport and dEposition Model (STEM). North American (NA) emissions heavily (>70% of total emissions) affect the BC levels from the surface to ∼5 km, while non-NA plumes compose more than half of the BC above ∼5 km. Among all sectors, NA and non-NA biomass burning, NA transportation and non-NA residential emissions are the major contributors. The sectoral contributions vary among ten regions defined by the US Environmental Protection Agency (EPA): NA anthropogenic emissions enhance northeastern US BC levels; biomass burning strongly impacts northern California and southeastern US; and the influence of extra-regional plumes is largest in the northwestern US but extends to eastern US. The mean contribution from non-NA sources to US surface BC is ∼0.05 μg m−3, with a maximum value of ∼0.11 μg m−3 in the northwestern US. The non-NA contributions to column BC are higher than to surface BC, ranging from 30% to 80%, depending on region. EPA region 8 is most sensitive to extra-regional BC, partially explaining the observed increasing BC trend there during the past decades associated with the increasing Asian BC emissions. Measurements from the June 24 DC-8 flight during the ARCTAS-CARB field campaign show that BC/(organic matter + nitrate + sulfate) mass ratios fairly well represent BC''s warming potential over southern California, which can be approximated by BC/(organic matter + sulfate) and BC/sulfate for plumes affected and unaffected by fires, respectively. The responses of BC/(organic matter + sulfate) and BC/sulfate to removing each emission sector are further discussed, indicating that mitigating NA transportation emissions has the highest potential for regional air quality and climate co-benefits. [Copyright &y& Elsevier]

Abstract: This study presents a system to predict high pollution events that develop in connection with enhanced subsidence due to coastal lows, particularly in winter over Santiago de Chile. An accurate forecast of these episodes is of interest since the local government is entitled by law to take actions in advance to prevent public exposure to PM10 concentrations in excess of 150 μg m−3 (24 h running averages). The forecasting system is based on accurately simulating carbon monoxide (CO) as a PM10/PM2.5 surrogate, since during episodes and within the city there is a high correlation (over 0.95) among these pollutants. Thus, by accurately forecasting CO, which behaves closely to a tracer on this scale, a PM estimate can be made without involving aerosol-chemistry modeling. Nevertheless, the very stable nocturnal conditions over steep topography associated with maxima in concentrations are hard to represent in models. Here we propose a forecast system based on the WRF–Chem model with optimum settings, determined through extensive testing, that best describe both meteorological and air quality available measurements. Some of the important configurations choices involve the boundary layer (PBL) scheme, model grid resolution (both vertical and horizontal), meteorological initial and boundary conditions and spatial and temporal distribution of the emissions. A forecast for the 2008 winter is performed showing that this forecasting system is able to perform similarly to the authority decision for PM10 and better than persistence when forecasting PM10 and PM2.5 high pollution episodes. Problems regarding false alarm predictions could be related to different uncertainties in the model such as day to day emission variability, inability of the model to completely resolve the complex topography and inaccuracy in meteorological initial and boundary conditions. Finally, according to our simulations, emissions from previous days dominate episode concentrations, which highlights the need for 48 h forecasts that can be achieved by the system presented here. This is in fact the largest advantage of the proposed system. [Copyright &y& Elsevier]

Abstract: In this paper, the NOx emission scaling factors applied over the 2001 National Emissions Inventory (NEI) are estimated through a four-dimensional variational (4D-Var) approach using SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric CHartographY) tropospheric NO2 columns measured during summer 2004. In the “top-down” approach, two-month average NO2 columns are assimilated into a regional chemical transport model (CTM), STEM, using different assimilation setups. In a basic setup, NOx emissions are adjusted by assimilating the NO2 columns. A more general setup of emission inversion allows the initial O3 concentrations be adjusted along with the NOx emissions. A final case is set up to assimilate both the NO2 columns and O3 measurement from various platforms while allowing adjustments of both the NOx emissions and the initial O3 concentrations. It is found that the addition of O3 measurements did not improve the NOx emission inversion. With the NOx emission at surface and upper levels being adjusted separately, results from four cases show that the elevated NOx emission reduction ranges from 8.9% to 11.4%, and the surface NOx emission reduction is up to 6.6%. All the cases show NOx emission reduction in Ohio valley and Washington, District of Columbia areas. [Copyright &y& Elsevier]

Aerosol distributions in Asia calculated over a 4-year period and constrained by satellite observations of aerosol optical depth (AOD) are presented. Vast regions in Asia that include > 80% of the population have PM2.5 concentrations that exceed on an annual basis the WHO guideline of 10 μg/m³, often by factors of 2 to 4. These high aerosol loadings also have important radiative effects, causing a significant dimming at the surface, and mask ∼45% of the warming by greenhouse gases. Black carbon (BC) concentrations are high throughout Asia, representing 5-10% of the total AOD, and contributing significantly to atmospheric warming (its warming potential is ∼55% of that due to CO2). PM levels and AODs in year 2030, estimated based on simulations that consider future changes in emissions, are used to explore opportunities for win—win strategies built upon addressing air quality and climate change together. It is found that in 2030 the PM2.5 levels in significant parts of Asia will increase and exacerbate health impacts; but the aerosols will have a larger masking effect on radiative forcing, due to a decrease in BC and an increase in SO2 emissions. [ABSTRACT FROM AUTHOR]

Abstract: Air quality prediction plays an important role in the management of our environment. Computational power and efficiencies have advanced to the point where chemical transport models can predict pollution in an urban air shed with spatial resolution less than a kilometer, and cover the globe with a horizontal resolution of less than 50km. Predicting air quality remains a challenge due to the complexity of the governing processes and the strong coupling across scales. While air quality prediction is closely aligned with weather prediction, there are important differences, including the role of pollution emissions and their associated large uncertainties. Improvements in air quality prediction require a close integration of observations. As more atmospheric chemical observations become available chemical data assimilation is expected to play an essential role in air quality forecasting. In this paper advances in air quality forecasting are discussed with an emphasis on data assimilation. Applications of the four-dimensional variational method (4D-Var) and the ensemble Kalman filter (EnKF) approach are presented and the computation challenges are discussed. [Copyright &y& Elsevier]

Abstract: The fate and transport of mercury in East Asia is evaluated using the Sulfur Transport and dEposition Model (STEM)-Hg 3-D model. The model calculates mercury transport, transformation and deposition in East Asia during April 2001, the period of the ACE-Asia field campaign. Model predictions of dry and wet deposition are compared with the observations from 10 sampling sites in Japan. The model results are consistent with the observations, but tend to over-predict dry deposition. Sensitivity analysis of predicted results to uncertainties in the mercury reaction rates suggests that the oxidation of Hg0 to Hg(II) in the gas phase is the dominant pathway for atmospheric mercury removal processes. Simulation based on the most recently published reaction rate constants for gas phase oxidation of Hg0 to Hg(II) overestimates the production of Hg(II) in the gas phase. The regional mercury budget is calculated which shows that most of the reactive gas phase mercury (RGM) and particulate mercury (Hgp) are deposited around the source region while 28.5Mg of Hg0 is exported out of East Asia during April 2001. The sensitivity analysis in the regional Hg budget to major uncertainties associated with Hg emission estimates and Hg chemistry are also evaluated. [Copyright &y& Elsevier]

Although continental-scale plumes of Asian dust and pollution reduce the amount of solar radiation reaching the earth's surface and perturb the chemistry of the atmosphere, our ability to quantify these effects has been limited by a lack of critical observations, particularly of layers above the surface. Comprehensive surface, airborne, shipboard, and satellite measurements of Asian aerosol chemical composition, size, optical properties, and radiative impacts were performed during the Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) study. Measurements within a massive Chinese dust storm at numerous widely spaced sampling locations revealed the highly complex structure of the atmosphere, in which layers of dust, urban pollution, and biomass-burning smoke may be transported long distances as distinct entities or mixed together. The data allow a first-time assessment of the regional climatic and atmospheric chemical effects of a continental-scale mixture of dust and pollution. Our results show that radiative flux reductions during such episodes are sufficient to cause regional climate change. [ABSTRACT FROM AUTHOR]

Measurements of gaseous SO2, NH3, and O3 using IVL passive sampler technology were obtained during a pilot measurement program initiated as a key component of the newly established WMO/GAW Urban Research Meteorology and Environment (GURME) project. Monthly measurements were obtained at 50 stations in Asia, Africa, South America, and Europe. The median SO2 concentrations vary from a high of 13 ppb at Linan, China, to <0.03 ppb at four stations. At 30 of 50 regional stations, the observed median concentrations are <1 ppb. Median ammonia concentrations range from 20 ppb at Dhangadi, India, to <1 ppb at nine stations. At 27 of regional stations, the ambient ammonia levels exceed 1 ppb. The median ozone concentrations vary from a maximum of 45 ppb at Waliguan Mountain, China, to 8 ppb in Petit Saut, French Guiana. In general, the highest ozone values are found in the mid-latitudes, with the Northern hemisphere mid-latitude values exceeding the Southern hemisphere mid-latitude levels, and the lowest values are typically found in the tropical regions. [Copyright &y& Elsevier]

The spatiotemporal distribution of observations plays an essential role in the data assimilation process. An adjoint sensitivity method is applied to the problem of adaptive location of the observational system for a nonlinear transport-chemistry model in the context of 4D variational data assimilation. The method is presented in a general framework and it is shown that in addition to the initial state of the model, sensitivity with respect to emission and deposition rates and certain types of boundary values may be obtained at a minimal additional cost. The adjoint modeling is used to evaluate the influence function and to identify the domain of influence associated with the observations. These essential tools are further used to develop a novel algorithm for targeting observations that takes into account the interaction among observations at different instants in time and spatial locations. Issues related to the case of multiple observations are addressed and it is shown that by using the adjoint modeling an efficient implementation may be achieved. Computational and practical aspects are discussed and this analysis indicates that it is feasible to implement the proposed method for comprehensive air quality models. Numerical experiments performed with a two-dimensional test model show promising results. [ABSTRACT FROM AUTHOR]

Asia is undergoing rapid urbanization resulting in increasing air pollution threats in its cities. The contribution of megacities to sulfur emissions and pollution in Asia is studied over a 25-year period (1975–2000) using a multi-layer Lagrangian puff transport model. Asian megacities cover <2% of the land area but emit ∼16% of the total anthropogenic sulfur emissions of Asia. It is shown that urban sulfur emissions contribute over 30% to the regional pollution levels in large parts of Asia. The average contribution of megacities over the western Pacific increased from <5% in 1975 to >10% in 2000. Two future emission scenarios are evaluated for 2020—“business as usual (BAU)” and “maximum feasible controls (MAXF)” to establish the range of reductions possible for these cities. The MAXF scenario would result in 2020 S-emissions that are ∼80% lower than those in 2000, at an estimated control cost of US $87 billion per year (1995 US$) for all of Asia. An urban scale analysis of sulfur pollution for four megacities—Shanghai, and Chongqing in China; Seoul in South Korea; and Mumbai (formerly Bombay) in India is presented. If pollution levels were allowed to increase under BAU, over 30 million people in these cities alone would be exposed to levels in excess of the WHO guidelines. [Copyright &y& Elsevier]

In the early 1990s, it was projected that annual SO[sub 2] emissions in Asia might grow to 80-110 Tg yr[sup -1] by 2020. Based on new high-resolution estimates from 1975 to 2000, we calculate that SO[sub 2] emissions in Asia might grow only to 40-45 Tg yr[sup -1] by 2020. The main reason for this lower estimate is a decline of SO[sub 2] emissions from 1995 to 2000 in China, which emits about two-thirds of Asian SO[sub 2]. The decline was due to a reduction in industrial coal use, a slowdown of the Chinese economy, and the closure of small and inefficient plants, among other reasons. One effect of the reduction in SO[sub 2] emissions in China has been a reduction in acid deposition not only in China but also in Japan. Reductions should also improve visibility and reduce health problems. SO[sub 2] emission reductions may increase global warming, but this warming effect could be partially offset by reductions in the emissions of black carbon. How SO[sub 2] emissions in the region change in the coming decades will depend on many competing factors (economic growth, pollution control laws, etc.). However a continuation of current trends would result in sulfur emissions lower than any IPCC forecasts. [ABSTRACT FROM AUTHOR]

Studies the role of mineral aerosol in tropospheric chemistry in East Asia. Presentation of the gas-phase and dust chemistry box model; Simulation of Asian dust; Characteristic distributions and uptake rate constants of Asian dust; Role of dust in tropospheric chemistry.

Looks at acid deposition in East Asia, presenting research which investigated long-range transport models for sulfur deposition in East Asia. Description of models utilized in this research; Examination of the chemical mechanism of these models; Statistical data compiled on atmospheric transport models; Conclusions of this research.

Looks at acid deposition, highlighting the development of the dry deposition model to estimate sulfur dioxide and sulfate dry deposition velocities over land use types in Asia. Information on acid deposition in Asia; Description of simulation procedures utilized in this research; Analysis of statistical data compiled in this research; Conclusions of this research.

High-resolution multi-component estimates of ground-level air pollutants are necessary for assessing their impacts to human health, agriculture, and ecosystems. We demonstrate a high-resolution fusion and downscaling approach over South Korea for May 2016 and May 2021. Daily 1 km fine particulate matter (PM 2.5), ozone (O 3), and nitrogen dioxide (NO 2) concentrations are calculated at ground level using a random forest machine learning (ML) algorithm, with predictors including reanalysis meteorology, satellite aerosol optical depth (AOD), and gridded surface fields from chemical transport models (CTM). The ML model is tested for May 2016, coinciding with the Korea-United States Air Quality Study (KORUS-AQ) intensive field campaign, and for May 2021, to allow incorporation of observations from the Geostationary Environment Monitoring Spectrometer (GEMS). In the tests for May 2016, the correlation coefficients (R) and root mean squared errors (RMSE) relative to withheld observations of daily-averaged pollutants in 10-fold cross-validation are promising: 0.93 (5.5 μg/m3), 0.90 (5.5 ppbv), and 0.95 (4.7 ppbv) for PM 2.5 , O 3 , and NO 2 , respectively. Relative performance is assessed for alternate choices of predictors: (a) 80-km global reanalysis Copernicus Atmosphere Monitoring Service (CAMS) vs. 4-km regional Weather Research and Forecasting model coupled with Chemistry (WRF-Chem); (b) AOD polar-orbiting Moderate Resolution Image Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) vs. geostationary GEMS; and (c) variations in surface observation density. This study is among the very first to incorporate both CTM and GEMS AOD for building surface high resolution multiple air pollution predictions over South Korea. • PM 2.5 , ozone, and NO 2 machine learning (random forest) retrospective data fusion and downscaling at 1 × 1 km2 over S. Korea. • Good performance (R 0.73–0.95), but decreases if less than three ground sites per million people. • Coarse spatial resolution global reanalysis fields are widely available and aid in machine learning fusion. [ABSTRACT FROM AUTHOR]

Dust loading in West and South Asia has been a major environmental issue due to its negative effects on air quality, food security, energy supply and public health, as well as on regional and global weather and climate. Yet a robust understanding of its recent changes and future projection remains unclear. On the basis of several high-quality remote sensing products, we detect a consistently decreasing trend of dust loading in West and South Asia over the last two decades. In contrast to previous studies emphasizing the role of local land use changes, here, we attribute the regional dust decline to the continuous intensification of Arctic amplification driven by anthropogenic global warming. Arctic amplification results in anomalous mid-latitude atmospheric circulation, particularly a deepened trough stretching from West Siberia to Northeast India, which inhibits both dust emissions and their downstream transports. Large ensemble climate model simulations further support the dominant role of greenhouse gases induced Arctic amplification in modulating dust loading over West and South Asia. Future projections under different emission scenarios imply potential adverse effects of carbon neutrality in leading to higher regional dust loading and thus highlight the importance of stronger anti-desertification counter-actions such as reforestation and irrigation management. [ABSTRACT FROM AUTHOR]

A new long-term emission inventory called the Inversed Emission Inventory for Chinese Air Quality (CAQIEI) was developed in this study by assimilating surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). This inventory contains the constrained monthly emissions of NOx, SO2, CO, primary PM2.5, primary PM10, and NMVOCs in China from 2013 to 2020, with a horizontal resolution of 15 km × 15 km. This paper documents detailed descriptions of the assimilation system and the evaluation results for the emission inventory. The results suggest that CAQIEI can effectively reduce the biases in the a priori emission inventory, with the normalized mean biases ranging from -9.1% to 9.5% in the a posteriori simulation, which are significantly reduced from the biases in the a priori simulations (-45.6% to 93.8%). The calculated RMSEs (0.3 mg/m³ for CO and 9.4-21.1 µg/m³ for other species, on the monthly scale) and correlation coefficients (0.76-0.94) were also improved from the a priori simulations, suggesting that CAQIEI can reasonably reproduce the magnitude and variation of emissions of different air pollutants in China. Based on CAQIEI, we estimated China's total emissions (including both natural and anthropogenic emissions) of the 6 species in 2015 to be as follows: 25.2 Tg of NOx, 17.8 Tg of SO2, 465.4 Tg of CO, 15.0 Tg of PM2.5, 40.1 Tg of PM10, and 46.0 Tg of NMVOCs. From 2015 to 2020, the total emissions reduced by 54.1% for SO2, 44.4% for PM2.5, 33.6% for PM10, 35.7% for CO, and 15.1% for NOx, but increased by 21.0% for NMVOCs. Larger emission reductions were achieved during the 2018-2020 action plan than during the 2013-2017 action plan for most species. In particular, NOx and NMVOC emissions were shown to increase during the 2013-2017 action plain, and there were obvious emission increases in the Fengwei Plain area over the Central China region. However, NOx and NMVOC emissions declined during the 2018-2020 action plan, and the emissions over the Fengwei Plain area also decreased. This suggests that the emission control policies were improved in the 2018-2020 action plan. We also compared CAQIEI with previous inventories, which verified our inversion results in terms of total emissions of NOx, SO2 and NMVOCs, and more importantly identified the potential uncertainties in our current understanding of China's air pollutant emissions. Firstly, CO emissions in China may be substantially underestimated by current inventories, with the CO emissions estimated by CAQIEI (426.8 Tg) being more than twice the amount in previous inventories (120.7-237.7 Tg). Significant underestimations for other air pollutant emissions may also exist over western and northeastern China. In addition, the NMVOC emissions were shown to be substantially underestimated over northern China but overestimated in southern China. Secondly, the emission reduction rates during 2015-2018 estimated by CAQIEI are generally smaller than those estimated by previous inventories, especially for NOx, PM10 and NMVOCs, suggesting that the mitigation effects of the air pollution control may be overestimated currently. In particular, China's NMVOC emissions were shown to have increased by 26.6% from 2015 to 2018, especially over the North China Plain by 38.0%), Northeast China (by 38.3%), and Central China (60.0%). In contrast, the emissions reduction rate of CO may be underestimated. Overall, our emissions inventory sheds new light on the complex variations of air pollutant emissions in China during its two recent clean air action periods, which could significantly improve our understanding of air pollutant emissions and related changes in air quality in China. [ABSTRACT FROM AUTHOR]

Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, satellite remote sensing AODs are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be obtained with satellite remote sensing under cloudy/hazy conditions or during nighttime. In this work, we introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We quantified the quantitative impact of input variables on the results using sensitivity and visual analysis of the model. This technique provides ground-level PM2.5 concentrations with a high spatial resolution (0.01°) and 24-h temporal coverage, hour-by-hour, complete coverage. In central and eastern China, the 10-fold cross-validation results show that R2 is between 0.8 and 0.9, and RMSE is between 6 and 26 (µg m−3). The relative error varies in different concentration ranges and is generally less than 20%. Better constrained spatiotemporal distributions of PM2.5 concentrations will contribute to improving health effects studies, atmospheric emission estimates, and air quality predictions. [ABSTRACT FROM AUTHOR]

Heat waves and air pollution extremes exert compounding effects on human health and food security and may worsen under future climate change. On the basis of reconstructed daily O3 levels in China and meteorological reanalysis, we found that the interannual variability of the frequency of summertime co-occurrence of heat wave and O3 pollution in China is regulated mainly by a combination of springtime warming in the western Pacific Ocean, western Indian Ocean, and Ross Sea. These sea surface temperature anomalies impose influences on precipitation, radiation, etc., to modulate the co-occurrence, which were also confirmed with coupled chemistry-climate numerical experiments. We thus built a multivariable regression model to predict co-occurrence a season in advance, and correlation coefficient could reach 0.81 (P < 0.01) for the North China Plain. Our results provide useful information for the government to take actions in advance to mitigate damage from these synergistic costressors. [ABSTRACT FROM AUTHOR]

A new open biomass burning inventory is presented that relies on the fire radiative power data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi NPP satellite. This VIIRS-based Fire Emission Inventory (VFEI) provides emission data from early 2012 to 2019 for more than 40 species of gases and aerosols at spatial resolutions of around 500 m. We found that VFEI produces similar results when compared to other major inventories in many regions of the world. Additionally, we conducted regional simulations using VFEI with the Weather Research and Forecasting (WRF) model with chemistry (WRF-Chem) for Southern Africa (September 2016) and North America (July–August 2019). We compared aerosol optical depth (AOD) from the model against two observational datasets: the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) product and AErosol RObotic NETwork (AERONET) stations. Results showed good agreement between both simulations and the datasets, with mean AOD biases of around +0.03 for Southern Africa and -0.01 for North America. Both simulations were not only able to reproduce the AOD magnitudes accurately, but also the inter-diurnal variations of smoke concentration. In addition, we made use of the airborne data from the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES; Southern Africa) and the Fire Influence on Regional to Global Environments Experiment and Air Quality (FIREX-AQ; North America) campaigns to evaluate the simulations. In Southern Africa, results showed correlations higher than 0.77 when comparing carbon monoxide and black carbon. In North America, correlations were lower and biases higher. However, this is because the model was not able to reproduce the timing, shape, and location of individual plumes over complex terrain (Rocky Mountains) during the FIREX-AQ campaign period. [ABSTRACT FROM AUTHOR]

Discusses some of the papers presented at the Fifth International Conference on Atmospheric Science and Air Quality (ASAAQ), which was held in Seattle, Washington, during 18?20 June 1996. Focus of the conference; Number of attendees; Highlights of some of the papers from the conference that are presented in this issue.

Accompanied by the continuous declines of PM2.5, O3 pollution has become increasingly prominent and has been targeted by the Government of China to protect climate, ecosystem, and human health. Although satellite retrievals of column O3 have been operated for decades and nationwide monitoring of ground-level O3 has been offered since 2013 in China, climatological variability of ground-level O3 remains unknown, which impedes understanding of the long-term driver and impacts of O3 pollution in China. Here we develop an eXtreme Gradient Boosting (XGBoost) model integrating high-resolution meteorological data, satellite retrievals of trace gases, etc. to provide reconstructed daily ground-level O3 over 2005-2021 in China. Model validation confirms the robustness of this dataset, with R² of 0.89 for sample-based crossvalidation. The accuracy of the long-term variations has also been confirmed with independent historical observations covering the same period from urban, rural and background sites. Our dataset covers the long time period of 2005-2021 with 0.1°×0.1° gap-free grids, which can facilitate climatological, ecological, and health research. The dataset is freely available at Zenodo (https://zenodo.org/record/6507706#.Yo8hKujP13g; Zhou, 2022). [ABSTRACT FROM AUTHOR]

Haze is a serious air pollution problem in China, especially in Beijing and surrounding areas, affecting visibility, public health and regional climate. In this study, the Weather Research and Forecasting-Chemistry (WRF-Chem) model was used to simulate PM 2.5 (particulate matters with aerodynamic diameter ≤ 2.5 μm) concentrations during the 2013 severe haze event in Beijing, and health impacts and health-related economic losses were calculated based on model results. Compared with surface monitoring data, the model results reflected pollution concentrations accurately (correlation coefficients between simulated and measured PM 2.5 were 0.7, 0.4, 0.5 and 0.6 in Beijing, Tianjin, Xianghe and Xinglong stations, respectively). Health impacts assessments show that the PM 2.5 concentrations in January might cause 690 (95% confidence interval (CI): (490, 890)) premature deaths, 45,350 (95% CI: (21,640, 57,860)) acute bronchitis and 23,720 (95% CI: (17,090, 29,710)) asthma cases in Beijing area. Results of the economic losses assessments suggest that the haze in January 2013 might lead to 253.8 (95% CI: (170.2, 331.2)) million US$ losses, accounting for 0.08% (95% CI: (0.05%, 0.1%)) of the total 2013 annual gross domestic product (GDP) of Beijing. [ABSTRACT FROM AUTHOR]

A new open biomass burning inventory is presented that relies on the fire radiative power data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi NPP satellite. This VIIRS-based Fire Emission Inventory (VFEI) provides emission data from early 2012 to 2019 for more than 40 species of gases and aerosols at spatial resolutions of around 500 m. We found that VFEI produces similar results when compared to other major inventories in many regions of the world. Additionally, we conducted regional simulations using VFEI with the Weather Research and Forecasting (WRF) model with chemistry (WRF-Chem) for Southern Africa (September 2016) and North America (July-August 2019). We compared aerosol optical depth (AOD) from the model against two observational datasets: the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) product and AErosol RObotic NETwork (AERONET) stations. Results showed good agreement between both simulations and the datasets, with mean AOD biases of around +0.03 for Southern Africa and -0.01 for North America. Both simulations were not only able to reproduce accurately the AOD magnitudes, but also the inter-diurnal variations of smoke concentration. In addition, we made use of the airborne data from the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES; Southern Africa) and the Fire Influence on Regional to Global Environments Experiment and Air Quality (FIREX-AQ; North America) campaigns to evaluate the simulations. In Southern Africa, results showed correlations higher than 0.77 when comparing carbon monoxide and black carbon. In North America, correlations were lower and biases higher. However, this is because the model was not able to reproduce the timing, shape, and location of individual plumes over complex terrain (Rocky Mountains) during the FIREX-AQ campaign period. [ABSTRACT FROM AUTHOR]

Abstract: Since the mid-1990s a new generation of Earth-observing satellites has been able to detect tropospheric air pollution at increasingly high spatial and temporal resolution. Most primary emitted species can be measured by one or more of the instruments. This review article addresses the question of how well we can relate the satellite measurements to quantification of primary emissions and what advances are needed to improve the usability of the measurements by U.S. air quality managers. Built on a comprehensive literature review and comprising input by both satellite experts and emission inventory specialists, the review identifies several targets that seem promising: large point sources of NO x and SO2, species that are difficult to measure by other means (NH3 and CH4, for example), area sources that cannot easily be quantified by traditional bottom-up methods (such as unconventional oil and gas extraction, shipping, biomass burning, and biogenic sources), and the temporal variation of emissions (seasonal, diurnal, episodic). Techniques that enhance the usefulness of current retrievals (data assimilation, oversampling, multi-species retrievals, improved vertical profiles, etc.) are discussed. Finally, we point out the value of having new geostationary satellites like GEO-CAPE and TEMPO over North America that could provide measurements at high spatial (few km) and temporal (hourly) resolution. [Copyright &y& Elsevier]

Passive air samplers (PAS) including polyurethane foam (PUF) are widely deployed as an inexpensive and practical way to sample semivolatile pollutants. However, concentration estimates from PAS rely on constant empirical mass transfer rates, which add unquantified uncertainties to concentrations. Here we present a method for modeling hourly sampling rates for semivolatile compounds from hourly meteorology using first-principle chemistry, physics, and fluid dynamics, calibrated from depuration experiments. This approach quantifies and explains observed effects of meteorology on variability in compound-specific sampling rates and analyte concentrations, simulates nonlinear PUF uptake, and recovers synthetic hourly concentrations at a reference temperature. Sampling rates are evaluated for polychlorinated biphenyl congeners at a network of Harner model samplers in Chicago, IL, during 2008, finding simulated average sampling rates within analytical uncertainty of those determined from loss of depuration compounds and confirming quasilinear uptake. Results indicate hourly, daily, and interannual variability in sampling rates, sensitivity to temporal resolution in meteorology, and predictable volatility-based relationships between congeners. We quantify the importance of each simulated process to sampling rates and mass transfer and assess uncertainty contributed by advection, molecular diffusion, volatilization, and flow regime within the PAS, finding that PAS chamber temperature contributes the greatest variability to total process uncertainty (7.3%). [ABSTRACT FROM AUTHOR]

The Lake Michigan Ozone Study 2017 (LMOS 2017) was a collaborative multiagency field study targeting ozone chemistry, meteorology, and air quality observations in the southern Lake Michigan area. The primary objective of LMOS 2017 was to provide measurements to improve air quality modeling of the complex meteorological and chemical environment in the region. LMOS 2017 science questions included spatiotemporal assessment of nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOC) emission sources and their influence on ozone episodes; the role of lake breezes; contribution of new remote sensing tools such as GeoTASO, Pandora, and TEMPO to air quality management; and evaluation of photochemical grid models. The observing strategy included GeoTASO on board the NASA UC-12 aircraft capturing NO2 and formaldehyde columns, an in situ profiling aircraft, two ground-based coastal enhanced monitoring locations, continuous NO2 columns from coastal Pandora instruments, and an instrumented research vessel. Local photochemical ozone production was observed on 2 June, 9–12 June, and 14–16 June, providing insights on the processes relevant to state and federal air quality management. The LMOS 2017 aircraft mapped significant spatial and temporal variation of NO2 emissions as well as polluted layers with rapid ozone formation occurring in a shallow layer near the Lake Michigan surface. Meteorological characteristics of the lake breeze were observed in detail and measurements of ozone, NOx, nitric acid, hydrogen peroxide, VOC, oxygenated VOC (OVOC), and fine particulate matter (PM2.5) composition were conducted. This article summarizes the study design, directs readers to the campaign data repository, and presents a summary of findings. [ABSTRACT FROM AUTHOR]

Abstract: In this study, the University of Iowa’s Chemical Weather Forecasting System comprising meteorological predictions using the WRF model, and off-line chemical weather predictions using tracer and full chemistry versions of the STEM model, designed to support the flight planning during the ARCTAS 2008 mission is described and evaluated. The system includes tracers representing biomass burning and anthropogenic emissions from different geographical emissions source regions, as well as air mass age indicators. We demonstrate how this forecasting system was used in flight planning and in the interpretation of the experimental data obtained through the case study of the summer mission ARCTAS DC-8 flight executed on July 9 2008 that sampled near the North Pole. The comparison of predicted meteorological variables including temperature, pressure, wind speed and wind direction against the flight observations shows that the WRF model is able to correctly describe the synoptic circulation and cloud coverage in the Arctic region The absolute values of predicted CO match the measured CO closely suggesting that the STEM model is able to capture the variability in observations within the Arctic region. The time–altitude cross sections of source region tagged CO tracers along the flight track helped in identifying biomass burning (from North Asia) and anthropogenic (largely China) as major sources contributing to the observed CO along this flight. The difference between forecast and post analysis biomass burning emissions can lead to significant changes (∼10–50%) in primary CO predictions reflecting the large uncertainty associated with biomass burning estimates and the need to reduce this uncertainty for effective flight planning. [Copyright &y& Elsevier]

Abstract: East Asia is the largest source region of global anthropogenic mercury emissions, and contributes to atmospheric mercury concentration and deposition in other regions. Similarly, mercury from the global pool also plays a role in the chemical transport of mercury in East Asia. Annual simulations of atmospheric mercury in East Asia were performed using the STEM-Hg modeling system to study the mass budgets of mercury in the region. The model results showed strong seasonal variation in mercury concentration and deposition, with signals from large point sources. The annual mean concentrations for gaseous elemental mercury, reactive gaseous mercury and particulate mercury in central China and eastern coastal areas were 1.8ngm−3, 100pgm−3 and 150pgm−3, respectively. Boundary conditions had a strong influence on the simulated mercury concentration and deposition, contributing to 80% of the concentration and 70% of the deposition predicted by the model. The rest was caused by the regional emissions before they were transported out of the model domain. Using different oxidation rates reported for the Hg0–O3 reaction (i.e., by Hall, 1995 vs. by Pal and Ariya, 2004) led to a 9% difference in the predicted mean concentration and a 40% difference in the predicted mean deposition. The estimated annual dry and wet deposition for East Asia in 2001 was in the range of 590–735Mg and 482–696Mg, respectively. The mercury mass outflow caused by the emissions in the domain was estimated to be 681–714Mgyr−1. This constituted 70% of the total mercury emission in the domain. The greatest outflow occurred in spring and early summer. [Copyright &y& Elsevier]

An inverse modeling method using the four-dimensional variational data assimilation approach is developed to provide a top-down estimate of mercury emission inventory in China. The mercury observations on board the C130 aircraft during the Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) campaign in April 2001 are assimilated into a regional chemical transport model, STEM. Using a 340Mg of elemental mercury emitted in 1999, the assimilation results in an increase in Hg0 emissions for China to 1140Mg in 2001. This is an upper limit amount of the elemental mercury required in China. The average emission-scaling factor is ∼3. 4 in China. The spatial changes in the mercury emissions after the assimilation are also evaluated. The largest changes are estimated on the China north-east coastal areas and the areas of north-center China. The influences of the observation and inventory uncertainties and the initial and boundary conditions on the emission estimates are discussed. Increasing the boundary conditions of Hg from 1. 2 to 1. 5ngm−3, results in a top-down estimate of Hg0 emissions for China of 718Mg, and leads the average scaling factor from 3. 4 to 2. 1. [Copyright &y& Elsevier]

The singular vectors of a chemical transport model are the directions of maximum perturbation growth over a finite time interval. They have proved useful for the estimation of error growth, the initialization of ensemble forecasts, and the optimal placement of adaptive observations. The aim of this paper is to address computational aspects of singular vector analysis for atmospheric chemical transport models. The distinguishing feature of these models is the presence of stiff chemical interactions. A projection approach to preserve the symmetry of the tangent linear–adjoint operator for stiff systems is discussed, and extended to 3D chemical transport simulations. Numerical results are presented for a simulation of atmospheric pollution in East Asia in March 2001. The singular values and the structure of the singular vectors depend on the length of the simulation interval, the meteorological data, the location of the optimization region and the selection of optimization species, the choice of error norms, and the size of the optimization region. [ABSTRACT FROM AUTHOR]

Abstract: The task of providing an optimal analysis of the state of the atmosphere requires the development of efficient computational tools that facilitate an efficient integration of observational data into models. In a variational approach the data assimilation problem is posed as a minimization problem, which requires the sensitivity (derivatives) of a cost functional with respect to problem parameters. The direct decoupled method has been extensively applied for sensitivity studies of air pollution. Adjoint sensitivity is a complementary approach which efficiently calculates the derivatives of a functional with respect to a large number of parameters. In this paper, we discuss the mathematical foundations of the adjoint sensitivity method applied to air pollution models, and present a complete set of computational tools for performing three-dimensional adjoint sensitivity studies. Numerical examples show that three-dimensional adjoint sensitivity analysis provides information on influence areas, which cannot be obtained solely by an inverse analysis of the meteorological fields. Several illustrative data assimilation results in a twin experiments framework, as well as the assimilation of a real data set are also presented. [Copyright &y& Elsevier]

Urban development in the mega-cities of Asia has caused detrimental effects on the human health of its inhabitants through air pollution. However, averting these health damages by investing in clean energy and industrial technologies and measures can be expensive. Many cities do not have the capital to make such investments or may prefer to invest that capital elsewhere. In this article, we examine the city of Shanghai, China, and perform an illustrative cost/benefit analysis of air pollution control. Between 1995 and 2020 we expect that Shanghai will continue to grow rapidly. Increased demands for energy will cause increased use of fossil fuels and increased emissions of air pollutants. In this work, we examine emissions of particles smaller than 10 μm in diameter (PM10), which have been associated with inhalation health effects. We hypothesize the establishment of a new technology strategy for coal-fired power generation after 2010 and a new industrial coal-use policy. The health benefits of pollution reduction are compared with the investment costs for the new strategies. The study shows that the benefit-to-cost ratio is in the range of 1–5 for the power-sector initiative and 2–15 for the industrial-sector initiative. Thus, there appear to be considerable net benefits for these strategies, which could be very large depending on the valuation of health effects in China today and in the future. This study therefore provides economic grounds for supporting investments in air pollution control in developing cities like Shanghai. [Copyright &y& Elsevier]

The analysis of comprehensive chemical reactions mechanisms, parameter estimation techniques, and variational chemical data assimilation applications require the development of efficient sensitivity methods for chemical kinetics systems. The new release (KPP-1.2) of the kinetic preprocessor (KPP) contains software tools that facilitate direct and adjoint sensitivity analysis. The direct-decoupled method, built using BDF formulas, has been the method of choice for direct sensitivity studies. In this work, we extend the direct-decoupled approach to Rosenbrock stiff integration methods. The need for Jacobian derivatives prevented Rosenbrock methods to be used extensively in direct sensitivity calculations; however, the new automatic and symbolic differentiation technologies make the computation of these derivatives feasible. The direct-decoupled method is known to be efficient for computing the sensitivities of a large number of output parameters with respect to a small number of input parameters. The adjoint modeling is presented as an efficient tool to evaluate the sensitivity of a scalar response function with respect to the initial conditions and model parameters. In addition, sensitivity with respect to time-dependent model parameters may be obtained through a single backward integration of the adjoint model. KPP software may be used to completely generate the continuous and discrete adjoint models taking full advantage of the sparsity of the chemical mechanism. Flexible direct-decoupled and adjoint sensitivity code implementations are achieved with minimal user intervention. In a companion paper, we present an extensive set of numerical experiments that validate the KPP software tools for several direct/adjoint sensitivity applications, and demonstrate the efficiency of KPP-generated sensitivity code implementations. [Copyright &y& Elsevier]

The Kinetic PreProcessor KPP was extended to generate the building blocks needed for the direct and adjoint sensitivity analysis of chemical kinetic systems. An overview of the theoretical aspects of sensitivity calculations and a discussion of the KPP software tools is presented in the companion paper.In this work the correctness and efficiency of the KPP generated code for direct and adjoint sensitivity studies are analyzed through an extensive set of numerical experiments. Direct-decoupled Rosenbrock methods are shown to be cost-effective for providing sensitivities at low and medium accuracies. A validation of the discrete–adjoint evaluated gradients is performed against the finite difference estimates. The accuracy of the adjoint gradients is measured using a reference gradient value obtained with a standard direct-decoupled method. The accuracy is studied for both constant step size and variable step size integration of the forward/adjoint model and the consistency between the discrete and continuous adjoint models is analyzed.Applications of the KPP-1.2 software package to direct and adjoint sensitivity studies, variational data assimilation, and parameter identification are considered for the comprehensive chemical mechanism SAPRC-99. [Copyright &y& Elsevier]

Describes a communication library for the parallelization of air quality models on structured grids. Background on the distribution, gathering and repartitioning routines for different domain decompositions implementing a master-worker strategy; Optimization strategies for different architectures.

Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions; 2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles; and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed. [ABSTRACT FROM AUTHOR]

The Chinese "coal-to-gas" and "coal-to-electricity" strategies aim at reducing dispersed coal consumption and related air pollution by promoting the use of clean and low-carbon fuels in northern China. Here, we show that on top of meteorological influences, the effective emission mitigation measures achieved an average decrease of fine particulate matter (PM2.5) concentrations of ∼14% in Beijing and surrounding areas (the "2+26" pilot cities) in winter 2017 compared to the same period of 2016, where the dispersed coal control measures contributed ∼60% of the total PM2.5 reductions. However, the localized air quality improvement was accompanied by a contemporaneous ∼15% upsurge of PM2.5 concentrations over large areas in southern China. We find that the pollution transfer that resulted from a shift in emissions was of a high likelihood caused by a natural gas shortage in the south due to the coal-to-gas transition in the north. The overall shortage of natural gas greatly jeopardized the air quality benefits of the coal-to-gas strategy in winter 2017 and reflects structural challenges and potential threats in China's clean-energy transition. [ABSTRACT FROM AUTHOR]

We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation, and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosol dynamics and thermodynamics, cloud chemistry and dynamics, diffusion, and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with algorithmic differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or finite-difference method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the complex variable method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM and CVM. In an example application of the full multiphase adjoint model, we provide the first estimates of how emissions of particulate matter (PM 2.5) affect public health across the US. [ABSTRACT FROM AUTHOR]

For the purpose of improving PM prediction skills in East Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM2.5 observations that accounts for the uncertainties in anthropogenic emissions. In contrast to the conventional method to estimate the BEC that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. The impacts of the new BEC were then tested for the prediction of surface PM2.5 over East Asia using Community Multi-scale Air Quality (CMAQ) initialized by three-dimensional variational method (3D-VAR). The surface PM2.5 data measured at 154 sites in South Korea and 1,535 sites in China were assimilated every six hours during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May-14 June 2016). Data assimilation with our new BEC showed better agreement with the surface PM2.5 observations than that with the conventional method. Our method also showed closer agreement with the observations in 24-hour PM2.5 predictions with ~ 44 % fewer negative biases than the conventional method. We conclude that increased standard deviations, together with horizontal and vertical length scales in the new BEC, tend to improve the data assimilation and short-term predictions for the surface PM2.5. This paper also suggests further research efforts devoted to estimating the BEC to improve PM2.5 predictions. [ABSTRACT FROM AUTHOR]

We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosols, cloud chemistry and dynamics, diffusion and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with Algorithmic Differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or Finite Difference Method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the Complex Variable Method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM/CVM. In an example application of the full, multiphase adjoint model, we provide the first estimates of how emissions of PM2.5 affect public health across the US. [ABSTRACT FROM AUTHOR]

Abstract Emissions from power plants in China and India contain a myriad of fine particulate matter (PM 2.5 , PM ≤ 2.5 μm in diameter) precursors, posing significant health risks among large, densely settled populations. Studies isolating the contributions of various source classes and geographic regions are limited in China and India, but such information could be helpful for policy makers attempting to identify efficient mitigation strategies. We quantified the impact of power generation emissions on annual mean PM 2.5 concentrations using the state-of-the-art atmospheric chemistry model WRF-Chem (Weather Research Forecasting model coupled with Chemistry) in China and India. Evaluations using nationwide surface measurements show the model performs reasonably well. We calculated province-specific annual changes in mortality and life expectancy due to power generation emissions generated PM 2.5 using the Integrated Exposure Response (IER) model, recently updated IER parameters from Global Burden of Disease (GBD) 2015, population data, and the World Health Organization (WHO) life tables for China and India. We estimate that 15 million (95% Confidence Interval (CI): 10 to 21 million) years of life lost can be avoided in China each year and 11 million (95% CI: 7 to 15 million) in India by eliminating power generation emissions. Priorities in upgrading existing power generating technologies should be given to Shandong, Henan, and Sichuan provinces in China, and Uttar Pradesh state in India due to their dominant contributions to the current health risks. Highlights • Health risks of emissions from power plants in China and India are estimated using the state-of-the-science framework. • Both mortality burdens and years of life lost (YLL) are calculated for each province/state in China and India. [ABSTRACT FROM AUTHOR]

To study regional-scale carbon dioxide (CO2) transport, temporal variability, and budget over the Southern California Air Basin (SoCAB) during the California Research at the Nexus of Air Quality and Climate Change (CalNex) 2010 campaign period, a model that couples the Weather Research and Forecasting (WRF) Model with the Vegetation Photosynthesis and Respiration Model (VPRM) has been used. Our numerical simulations use anthropogenic CO2 emissions of the Hestia Project 2010 fossil-fuel CO2 emissions data products along with optimized VPRM parameters at 'FLUXNET' sites, for biospheric CO2 fluxes over SoCAB. The simulated meteorological conditions have been validated with ground and aircraft observations, as well as with background CO2 concentrations from the coastal Palos Verdes site. The model captures the temporal pattern of CO2 concentrations at the ground site at the California Institute of Technology in Pasadena, but it overestimates the magnitude in early daytime. Analysis of CO2 by wind directions reveals the overestimate is due to advection from the south and southwest, where downtown Los Angeles is located. The model also captures the vertical profile of CO2 concentrations along with the flight tracks. The optimized VPRM parameters have significantly improved simulated net ecosystem exchange at each vegetation-class site and thus the regional CO2 budget. The total biospheric contribution ranges approximately from −24% to −20% (daytime) of the total anthropogenic CO2 emissions during the study period. [ABSTRACT FROM AUTHOR]