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The quantification of ammonia (NH3) emissions is essential to the more accurate quantification of atmospheric nitrogen deposition, improved air quality and the assessment of ammonia-related agricultural policy and climate mitigation strategies. The quantity, geographic distribution and historical trends of these emissions remain largely uncertain. In this paper, a new Chinese agricultural fertilizer NH3 (CAF NH3) emissions inventory has been compiled that exhibits the following improvements: (1) a 1 km × 1 km gridded map on the county level was developed for 2008, (2) a combined bottom-up and top-down method was used for the local correction of emission factors (EFs) and parameters, (3) the spatial and temporal patterns of historical time trends for 1978-2008 were estimated and the uncertainties were quantified for the inventories, and (4) a sensitivity test was performed in which a province-level disaggregated map was compared with CAF NH3 emissions for 2008. The total CAF NH3 emissions for 2008 were 8.4 Tg NH3 yr-1 (a 6.6-9.8 Tg interquartile range). From 1978 to 2008, annual NH3 emissions fluctuated with three peaks (1987, 1996 and 2005), and total emissions increased from 3.2 to 8.4 Tg at an annual rate of 3.0 %. During the study period, the contribution of livestock manure spreading increased from 37.0 to 45.5 % because of changing fertilization practices and the rapid increase in egg, milk and meat consumption. The average contribution of synthetic fertilizer, which has a positive effect on crop yields, was approximately 38.3 % (minimum: 33.4 %; maximum: 42.7 %). With rapid urbanization causing a decline in the rural population, the contribution of the rural excrement sector varied widely between 20.3 and 8.5 %. The average contributions of cake fertilizer and straw returning were approximately 3.8 and 4.5 %, respectively, thus small and stable. Collectively, the CAF-NH3 emissions reflect the nation's agricultural policy to a certain extent. An effective approach to decreasing PM2.5 concentrations in China would be to simultaneously decrease NOx, SO2 and NH3 emissions. [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NOx = NO + NO2) are air pollutants which are co-emitted with CO2 during high-temperature combustion processes. Monitoring NOx emissions is crucial for assessing air quality and for providing proxy estimates of CO2 emissions. Satellite observations, such as those from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5P satellite, provide global coverage at high temporal resolution. However, satellites measure only NO2 , necessitating a conversion to NOx. Previous studies have applied a constant NO2 -to- NOx conversion factor. In this paper, we develop a more realistic model for NO2 -to- NOx conversion and apply it to TROPOMI data of 2020 and 2021. To achieve this, we analysed plume-resolving simulations from the MicroHH large-eddy simulation model with chemistry for the Bełchatów (PL), Jänschwalde (DE), Matimba (ZA) and Medupi (ZA) power plants, as well as a metallurgical plant in Lipetsk (RU). We used the cross-sectional flux method to calculate NO , NO2 and NOx line densities from simulated NO and NO2 columns and derived NO2 -to- NOx conversion factors as a function of the time since emission. Since the method of converting NO2 to NOx presented in this paper assumes steady-state conditions and that the conversion factors can be modelled by a negative exponential function, we validated the conversion factors using the same MicroHH data. Finally, we applied the derived conversion factors to TROPOMI NO2 observations of the same sources. The validation of the NO2 -to- NOx conversion factors shows that they can account for the NOx chemistry in plumes, in particular for the conversion between NO and NO2 near the source and for the chemical loss of NOx further downstream. When applying these time-since-emission-dependent conversion factors, biases in NOx emissions estimated from TROPOMI NO2 images are greatly reduced from between - 50 % and - 42 % to between only - 9.5 % and - 0.5 % in comparison with reported emissions. Single-overpass estimates can be quantified with an uncertainty of 20 %–27 %, while annual NOx emission estimates have uncertainties in the range of 4 %–21 % but are highly dependent on the number of successful retrievals. Although more simulations covering a wider range of meteorological and trace gas background conditions will be needed to generalise the approach, this study marks an important step towards a consistent, uniform, high-resolution and near-real-time estimation of NOx emissions – especially with regard to upcoming NO2 -monitoring satellites such as Sentinel-4, Sentinel-5 and CO2M. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to describe the influence of different characteristics of assimilation system on the surface ozone in Europe. It states that the evaluation of the background error covariance matrix (BECM) was emphasized. The result of the study shows that the data assimilation system was efficient in bring the model assimilations closer to observations.

To improve short-term particulate matter (PM) forecasts in South Korea, the initial distribution of PM composition, particularly over the upwind regions, is primarily important. To prepare the initial PM composition, the aerosol optical depth (AOD) data retrieved from a geostationary equatorial orbit (GEO) satellite sensor, GOCI (Geostationary Ocean Color Imager) which covers Northeast Asia (113-146° E; 25-47° N), were used. Although GOCI can provide a higher number of AOD data in a semi-continuous manner than low Earth orbit (LEO) satellite sensors, it still has a serious limitation in that the AOD data are not available at cloud pixels and over high-reflectance areas, such as desert and snow-covered regions. To overcome this limitation, a spatio-temporal (ST) kriging method was used to better prepare the initial AOD distributions that were converted into the PM composition over Northeast Asia. One of the largest advantages to using the ST-kriging method in this study is that more observed AOD data can be used to prepare the best initial AOD fields. It is demonstrated in this study that the short-term PM forecast system developed with the application of the ST-kriging method can greatly improve PM10 predictions in Seoul Metropolitan Area (SMA), when evaluated with ground-based observations. For example, errors and biases of PM10 predictions decreased by ∼ 60 and ∼ 70 %, respectively, during the first 6 h of short-term PM forecasting, compared with those without the initial PM composition. In addition, the influences of several factors (such as choices of observation operators and control variables) on the performances of the short-term PM forecast were explored in this study. The influences of the choices of the control variables on the PM chemical composition were also investigated with the composition data measured via PILS-IC and low air-volume sample instruments at a site near Seoul. To improve the overall performances of the short-term PM forecast system, several future research directions were also discussed and suggested. [ABSTRACT FROM AUTHOR]

A new anisotropic hr-adaptive mesh technique has been applied to modelling of multiscale transport phenomena, which is based on a discontinuous Galerkin/control volume discretization on unstructured meshes. Over existing air quality models typically based on static-structured grids using a locally nesting technique, the advantage of the anisotropic hr-adaptive model has the ability to adapt the mesh according to the evolving pollutant distribution and flow features. That is, the mesh resolution can be adjusted dynamically to simulate the pollutant transport process accurately and effectively. To illustrate the capability of the anisotropic adaptive unstructured mesh model, three benchmark numerical experiments have been setup for two-dimensional (2-D) transport phenomena. Comparisons have been made between the results obtained using uniform resolution meshes and anisotropic adaptive resolution meshes. [ABSTRACT FROM AUTHOR]

The Emission Scenario Projection (ESP) method produces future-year air pollutant emissions for mesoscale air quality modeling applications. We present ESP v2.0, which expands upon ESP v1.0 by spatially allocating future-year emissions to account for projected population and land use changes. In ESP v2.0, US Census Division-level emission growth factors are developed using an energy system model. Regional factors for population-related emissions are spatially disaggregated to the county level using population growth and migration projections. The county-level growth factors are then applied to grow a base-year emission inventory to the future. Spatial surrogates are updated to account for future population and land use changes, and these surrogates are used to map projected county-level emissions to a modeling grid for use within an air quality model. We evaluate ESP v2.0 by comparing US 12 km emissions for 2005 with projections for 2050. We also evaluate the individual and combined effects of county-level disaggregation and of updating spatial surrogates. Results suggest that the common practice of modeling future emissions without considering spatial redistribution over-predicts emissions in the urban core and under-predicts emissions in suburban and exurban areas. In addition to improving multi-decadal emission projections, a strength of ESP v2.0 is that it can be applied to assess the emissions and air quality implications of alternative energy, population and land use scenarios. [ABSTRACT FROM AUTHOR]

We conducted simulations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.5 to study air quality in East and South Asia at a spatial resolution of 20 km x 20 km. We find large discrepancies between two existing emissions inventories: the Regional Emission Inventory in Asia version 2 (REAS) and the Emissions Database for Global Atmospheric Research version 4.2 (EDGAR) at the provincial level in China, with maximum differences up to 500% for CO emissions, 190% for NO, and 160% for primary PM10. Such differences in the magnitude and the spatial distribution of emissions for various species lead to 40-70% difference in surface PM10 concentrations, 16-20% in surface O3 mixing ratios, and over 100% in SO2 and NO2 mixing ratios in the polluted areas of China. Our sensitivity run shows WRF-Chem is sensitive to emissions, with the REAS-based simulation reproducing observed concentrations and mixing ratios better than the EDGAR-based simulation for July 2007. We conduct further model simulations using REAS emissions for January, April, July, and October in 2007 and evaluate simulations with available ground-level observations. The model results show clear regional variations in the seasonal cycle of surface PM10 and O3 over East and South Asia. The model meets the air quality model performance criteria for both PM10 (mean fractional bias, MFB≤±60%) and O3 (MFB≤±15%) in most of the observation sites, although the model underestimates PM10 over Northeast China in January. The model predicts the observed SO2 well at sites in Japan, while it tends to overestimate SO2 in China in July and October. The model underestimates most observed NO2 in all four months. These findings suggest that future model development and evaluation of emission inventories and models are needed for particulate matter and gaseous pollutants in East and South Asia. [ABSTRACT FROM AUTHOR]

The study of aerosols in the troposphere and in the stratosphere is of major importance both for climate and air quality studies. Among the numerous instruments available, aerosol particles counters provide the size distribution in diameter range from few hundreds of nm to few tens of μm. Most of them are very sensitive to the nature of aerosols, and this can result in significant biases in the retrieved size distribution. We describe here a new versatile optical particle/sizer counter (OPC) named LOAC (Light Optical Aerosol Counter), which is light and compact enough to perform measurements not only at the surface but under all kinds of balloons in the troposphere and in the stratosphere. LOAC is an original OPC performing observations at two scattering angles. The first one is around 12°, and is almost insensitive to the nature of the particles; the second one is around 60° and is strongly sensitive to the refractive index of the particles. By combining measurement at the two angles, it is possible to retrieve accurately the size distribution and to estimate the nature of the dominant particles (droplets, carbonaceous, salts and mineral particles) in several size classes. This topology is based on calibration charts obtained in the laboratory. Several campaigns of cross-comparison of LOAC with other particle counting instruments and remote sensing photometers have been conducted to validate both the size distribution derived by LOAC and the retrieved particle number density. The topology of the aerosols has been validated in well-defined conditions including urban pollution, desert dust episodes, fog, and cloud. Comparison with reference aerosol mass monitoring instruments also shows that the LOAC measurements can be successfully converted to mass concentrations. All these tests indicate that no bias is present in the LOAC measurements and in the corresponding data processing. [ABSTRACT FROM AUTHOR]

Finding the number and significant locations of fixed air quality monitoring stations at ground level is challenging because of the complexity of the urban environment and the large number of factors affecting the pollutants concentration. Datasets of urban parameters such as land use, building morphology and street geometry in high resolution grid cells in combination with direct measurements of airborne pollutants in high frequency (1-10 s) along a reasonable number of streets can be used to interpolate concentration of pollutants in a whole gridded domain and determine the optimum number of monitoring sites and best locations for a network of fixed monitors at ground level. In this context, a data-driven modeling methodology is developed based on the application of Self Organizing Map (SOM) to approximate the nonlinear relations between urban parameters (80 in this work) and aerosol pollution data, such as mass and number concentrations measured along streets of a commercial/residential neighborhood of Singapore. Cross-validations between measured and predicted aerosol concentrations based on the urban parameters at each individual grid cell showed satisfying results. The urban parameters used in this case proved to be an appropriate indirect measure of aerosol concentrations within the studied area. The potential locations for fixed air quality monitors are identified through clustering of areas (i.e. group of cells) with similar urban patterns. The typological center of each cluster corresponds to the most representative cell for all other cells in the cluster. In the studied neighborhood four different clusters were identified and for each cluster potential sites for air quality monitoring at ground level are identified. [ABSTRACT FROM AUTHOR]

This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. The paper gives an overall picture of its status at the end of MACC-II (summer 2014). This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs and PAN+PAN precursors) over 8 vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performances of the system are assessed daily, weekly and 3 monthly (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the median ensemble to forecast regional ozone pollution events. The time period of this case study is also used to illustrate that the median ensemble generally outperforms each of the individual models and that it is still robust even if two of the seven models are missing. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10 and show an overall improvement over time. The change of the skills of the ensemble over the past two summers for ozone and the past two winters for PM10 are discussed in the paper. While the evolution of the ozone scores is not significant, there are improvements of PM10 over the past two winters that can be at least partly attributed to new developments on aerosols in the seven individual models. Nevertheless, the year to year changes in the models and ensemble skills are also linked to the variability of the meteorological conditions and of the set of observations used to calculate the statistical indicators. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion. [ABSTRACT FROM AUTHOR]

The distribution and quantity of trees within a city impacts issues such as urban heat islands, air quality and general city planning. Having an automatic procedure for cataloguing them can be a valuable aid for future urban planning and design. In this paper, the forestry surveying neural network, DeepForest, is utilised for tree detection in the urban environment. The study area covers District Lozenets, which is the greenest part of Sofia, Bulgaria. Three distinct approaches are implemented considering the urban vegetation context - a simple tree detection, a tree cluster detection and a mixing approach between the two based on approximation with Poisson Disk Sampling. The evaluation of the developed models, in terms of F-1 score, shows that the achieved results are comparable to the ones achieved by the original application of the DeepForest model. Due to the specifics of urban data, all models tended to achieve a higher precision but a lower recall than the original DeepForest. Conditions, such as shade from the sun, buildings or other trees, make the detection more challenging. The obtained results prove the feasibility of the proposed approaches, even with a small amount of labelled data. The tree cluster and mixed approaches have the potential to resolve part of the issues coming from the urban environment context of application. [ABSTRACT FROM AUTHOR]

The article presents information on a new procedure for the simultaneous estimation of sulphur dioxide and ash abundances in a volcanic plume using thermal infrared MODIS data. Plume altitude and temperature are stated to be the required input parameters to run the procedure. In the study a simple relation between the ash transmittances is demonstrated and used for the sulphur dioxide columnar content estimation.

An automated function control unit was developed to regularly check the ambient particle number concentration derived from a mobility particle size spectrometer as well as its zero-point behaviour. The aim of the new feature is to conduct unattended quality control experiments under field conditions at remote air quality monitoring or research stations. The automated function control also has the advantage of being able to get a faster system stability response than the recommended on-site comparisons with reference instruments. The method is based on a comparison of the total particle number concentration measured by a mobility particle size spectrometer and a condensation particle counter removing the diffusive particles approximately smaller than 25 nm in diameter. In practice, the small particles are removed by a set of diffusion screens, as traditionally used in a diffusion battery. The other feature of the automated function control is to check the zero-point behaviour of the ambient aerosol passing through a high-efficiency particulate air (HEPA) filter. An exemplary one-year data set is presented for the measurement site Annaberg- Buchholz as part of the Saxon air quality monitoring network. The total particle number concentration derived from the mobility particle size spectrometer overestimates the particle number concentration by only 2% (grand average offset). Furthermore, tolerance criteria are presented to judge the performance of the mobility particle size spec trometer with respect to the particle number concentration. An upgrade of a mobility particle size spectrometer with an automated function control enhances the quality of long-term particle number size distribution measurements. Quality assured measurements are a precondition for intercomparison studies of different sites. Comparable measurements will improve cohort health and also climate-relevant research studies. [ABSTRACT FROM AUTHOR]

Mixing height is one of the key parameters in describing lower tropospheric dynamics, and capturing the diurnal variability is crucial, especially in interpreting surface observations. In this paper we introduce a method for identifying mixing heights below the vertical minimum range of a scanning Doppler lidar. The method we propose is based on velocity variance in low elevation angle conical scanning, and is applied to measurements in two very different coastal environments: Limassol, Cyprus during summer; and Loviisa, Finland during winter. At both locations, the new method agrees well with mixing heights derived from turbulent kinetic energy dissipation rate profiles obtained from vertically-pointing measurements. The low-level scanning routine frequently indicated non-zero mixing heights less than 100m above the surface. Such low mixing heights were more common at wintertime Loviisa on the Baltic Sea coast than during summertime in Mediterranean Limassol. [ABSTRACT FROM AUTHOR]

The article presents information on a study to investigate the method of all-sky imaging for aerosol property retrieval. The study aimed at proposing a technical frame work for image processing as well as analysis. The study showed that the feature vector of a polarized all-sky imager is appropriate for aerosol classification according to size and single scatter albedo.

The article presents information on a study on measurements of air pollution emission factors for marine transportation. In the study, the chemical composition of the plumes of seagoing ships was investigated in a two weeks long measurement campaign in the port of Rotterdam in Holland. Some of the concerned main atmospheric components are sulphur dioxide and nitrogen dioxide.

Mechanistic air pollution models are essential tools in air quality management. Widespread use of such models is hindered, however, by the extensive expertise or computational resources needed to run most models. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations - the air pollution outcome generally causing the largest monetized health damages - attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model (WRF-Chem) within an Eulerian modeling framework, to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. InMAP uses a variable resolution grid that focuses on human exposures by employing higher spatial resolution in urban areas and lower spatial resolution in rural and remote locations and in the upper atmosphere; and by directly calculating steady-state, annual average concentrations. In comparisons run here, InMAP recreates WRF-Chem predictions of changes in total PM2.5 concentrations with population-weighted mean fractional error (MFE) and bias (MFB)< 10% and population-weighted R² ≈ 0.99. Among individual PM2.5 species, the best predictive performance is for primary PM2.5 (MFE: 16%; MFB: 13%) and the worst predictive performance is for particulate nitrate (MFE: 119%; MFB: 106%). Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. Features planned for future model releases include a larger spatial domain, more temporal information, and the ability to predict ground-level ozone (O3) concentrations. The InMAP model source code and input data are freely available online. [ABSTRACT FROM AUTHOR]

Community Multiscale Air Quality (CMAQ) model simulations utilizing the volatility basis set (VBS) treatment for organic aerosols (CMAQ-VBS) were evaluated against measurements collected at routine monitoring networks (Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE)) and those collected during the 2010 California at the Nexus of Air Quality and Climate Change (CalNex) field campaign to examine important sources of organic aerosol (OA) in southern California. CMAQ-VBS (OA lumped by volatility, semivolatile POA) underpredicted total organic carbon (OC) at CSN (-25.5% Normalized Median Bias (NMdnB)) and IMPROVE (-63.9% NMdnB) locations and total OC was underpredicted to a greater degree compared to the CMAQ-AE6 (9.9 and -55.7% NMdnB, respectively; semi-explicit OA treatment, SOA lumped by parent hydrocarbon, nonvolatile POA). However, comparisons to aerosol mass spectrometer (AMS) measurements collected at Pasadena, CA indicated that CMAQ-VBS better represented the diurnal profile and the primary/secondary split of OA. CMAQ-VBS secondary organic aerosol (SOA) underpredicted the average measured AMS oxygenated organic aerosol (OOA, a surrogate of SOA) concentration by a factor of 5.2 (4.7 μgm-3 measured vs. 0.9 μgm-3 modeled), a considerable improvement to CMAQ-AE6 SOA predictions, which were approximately 24× lower than the average AMS OOA concentration. We use two new methods, based on species ratios and on a simplified SOA parameterization from the observations, to apportion the SOA underprediction for CMAQ-VBS to too slow photochemical oxidation (estimated as 1.5× lower than observed at Pasadena using -log(NOx : NOy )), low intrinsic SOA formation efficiency (low by 1.6 to 2× for Pasadena), and too low emissions or too high 25 dispersion for the Pasadena site (estimated to be 1.6 to 2.3× too low/high). The first and third factors will be similar for CMAQ-AE6, while the intrinsic SOA formation efficiency for that model is estimated to be too low by about 7×. For CMAQ-VBS, 90% of the anthropogenic SOA mass formed was attributed to aged secondary semivolatile vapors (70% originating from volatile organic compounds (VOCs) and 20% from intermediate volatility compounds (IVOCs)). From source-apportioned model results, we found most of the CMAQ-VBS modeled POA at the Pasadena CalNex site was attributable to meat cooking emissions (48%, and consistent with a substantial fraction of cooking OA in the observations), compared to 18% from gasoline vehicle emissions, 13% from biomass burning (in the form of residential wood combustion), and 8% from diesel vehicle emissions. All "other" inventoried emission sources (e.g. industrial/point sources) comprised the final 13 %. The CMAQ-VBS semivolatile POA treatment underpredicted AMS hydrocarbon-like OA (HOA)+cooking-influenced OA (CIOA) at Pasadena by a factor of 1.8 (1.16 μgm-3 modeled vs. 2.05 μgm-3 observed) compared to a factor of 1.4 overprediction of POA in CMAQ-AE6, but did well to capture the AMS diurnal profile of HOA and CIOA, with the exception of the midday peak. We estimated that using the National Emission Inventory (NEI) POA emissions without scaling to represent SVOCs underestimates SVOCs by ~ 1.7×. [ABSTRACT FROM AUTHOR]

The occurrence of African dust outbreaks over the western Mediterranean basin were identified on an 11 year period (2001-2011). PM10 daily data from nine regional background air quality monitoring sites across the study area were compiled and the net dust load transported during each event was estimated. Then, the main atmospheric circulation patterns causing the transport of African air masses, were characterized by mean of an objective classification methodology of atmospheric variables fields. Next, the potential source areas of mineral dust, associated to each circulation pattern were identified by trajectory statistical methods. Finally, an impact index was calculated to 10 estimate the incidence of the African dust outbreaks produced during each circulation pattern, on the levels of dust load in PM10 concentrations recorded in the different regions. Our results indicate that the values of the impact index and the areas affected by African dust, strongly depended on the atmospheric circulation pattern. Four circulation types were obtained by the classification procedure. Two of them (CT-1 and CT-4) occurred predominantly during the warm season, bringing dust from areas of Algeria, Tunisia, Western Sahara, western Libya and Mauritania. African dust outbreaks produced during the CT-4 were the most frequent across the period of study, generating the highest impact index over southern, central and eastern regions of the Iberian Peninsula as well as over the Balearic Islands. Conversely, the events caused by the CT-1 encompassed the highest impact index over the western areas of the Iberian Peninsula. The two remaining circulation types (CT-2 and CT-3) were more frequently observed during the spring season. The prevailing flows generated by these two atmospheric circulation patterns, carried mineral dust from areas of Algeria, Tunisia and Western Sahara, giving rise to higher values of the impact index from eastern to western areas of the western Mediterranean basin. [ABSTRACT FROM AUTHOR]

Field deployments of the Aerodyne Aerosol Mass Spectrometer (AMS) have significantly advanced real-time measurements and source apportionment of non-refractory particulate matter. However, the cost and complex maintenance requirements of the AMS make impractical its deployment at suffcient sites to determine regional characteristics. Furthermore, the negligible transmission efficiency of the AMS inlet for supermicron particles significantly limits the characterization of their chemical nature and contributing sources. In this study, we utilize the AMS to characterize the water-soluble organic fingerprint of ambient particles collected onto conventional quartz filters, which are routinely sampled at many air quality sites. The method was applied to 256 particulate matter (PM) filter samples (PM1, PM2.5, PM10) collected at 16 urban and rural sites during summer and winter. We show that the results obtained by the present technique compare well with those from co-located online measurements, e.g. AMS or Aerosol Chemical Speciation Monitor (ACSM). The bulk recoveries of organic aerosol (60-91%) achieved using this technique, together with low detection limits (0.8 μg of organic aerosol on the analyzed filter fraction) allow its application to environmental samples. We will discuss the recovery variability of individual hydrocarbon, oxygen containing and other ions. The performance of such data in source apportionment is assessed in comparison to ACSM data. Recoveries of organic components related to different sources as traffic, wood burning and secondary organic aerosol are presented. This technique, while subjected to the limitations inherent to filter-based measurements (e.g. filter artifacts and limited time resolution) may be used to enhance the AMS capabilities in measuring size-fractionated, spatially-resolved long-term datasets. [ABSTRACT FROM AUTHOR]

Photometric ozone measurements rely upon an accurate value of the ozone absorption cross section at 253.65 nm. This has recently been reevaluated by Viallon et al. (2015) as 1.8% smaller than the accepted value (Hearn, 1961) used for the preceding fifty years. Thus, ozone measurements that applied the older cross section systematically underestimate the amount of ozone in air. We correct the reported historical surface data from North America and Europe and find that this modest change in cross section has a significant impact on the number of locations that are out of compliance with air quality regulations if the air quality standards remain the same. We find 18, 23, and 20% increases in the number of sites that are out of compliance with current US, Canadian, and European ozone air quality health standards for the year 2012. Should the new cross section value be applied, it would impact attainment of air quality standards and compliance with relevant clean air acts, unless the air quality target values themselves were also changed proportionately. We draw attention to how a small change in gas metrology has a global impact on attainment and compliance with legal air quality standards. We suggest that further laboratory work to evaluate the new cross section is needed and suggest three possible technical and policy responses should the new cross section be adopted. [ABSTRACT FROM AUTHOR]

Increased natural gas production in recent years has spurred intense interest in methane (CH4) emissions associated with its production, gathering, processing, transmission and distribution. Gathering and processing facilities (G&P facilities) are unique in that the wide range of gas sources (shale, coal-bed, tight gas, conventional, etc.) results in a wide range of gas compositions, which in turn requires an array of technologies to prepare the gas for pipeline transmission and distribution. We present an overview and detailed description of the measurement method and analysis approach used during a 20-week field campaign studying CH4 emissions from the natural gas G&P facilities between October 2013 and April 2014. Dual tracer flux measurements and onsite observations were used to address the magnitude and origins of CH4 emissions from these facilities. The use of a second tracer as an internal standard revealed plume-specific uncertainties in the measured emission rates of 20-47 %, depending upon plume classification. Combining downwind methane, ethane (C2H6), carbon monoxide (CO), carbon dioxide (CO2), and tracer gas measurements with onsite tracer gas release allows for quantification of facility emissions, and in some cases a more detailed picture of source locations. [ABSTRACT FROM AUTHOR]

In times of growing concern about the impacts of air pollution across the globe, lower-cost sensor technology is giving the first steps in helping to enhance our understanding and ability to manage air quality issues, particularly in regions without established monitoring networks. While the benefits of greater spatial coverage and real-time measurements that these systems offer are evident, challenges still need to be addressed regarding sensor reliability and data quality. Given the limitations imposed by intellectual property, commercial implementations are often "black boxes", which represents an extra challenge as it limits end users' understanding of the data production process. In this paper we present an overview of the QUANT (Quantification of Utility of Atmospheric Network Technologies) study, a comprehensive 3-year assessment across a range of urban environments in the United Kingdom, evaluating 43 sensor devices, including 119 gas sensors and 118 particulate matter (PM) sensors, from multiple companies. QUANT stands out as one of the most comprehensive studies of commercial air quality sensor systems carried out to date, encompassing a wide variety of companies in a single evaluation and including two generations of sensor technologies. Integrated into an extensive dataset open to the public, it was designed to provide a long-term evaluation of the precision, accuracy and stability of commercially available sensor systems. To attain a nuanced understanding of sensor performance, we have complemented commonly used single-value metrics (e.g. coefficient of determination, R2 ; root mean square error, RMSE; mean absolute error, MAE) with visual tools. These include regression plots, relative expanded uncertainty (REU) plots and target plots, enhancing our analysis beyond traditional metrics. This overview discusses the assessment methodology and key findings showcasing the significance of the study. While more comprehensive analyses are reserved for future detailed publications, the results shown here highlight the significant variation between systems, the incidence of corrections made by manufacturers, the effects of relocation to different environments and the long-term behaviour of the systems. Additionally, the importance of accounting for uncertainties associated with reference instruments in sensor evaluations is emphasised. Practical considerations in the application of these sensors in real-world scenarios are also discussed, and potential solutions to end-user data challenges are presented. Offering key information about the sensor systems' capabilities, the QUANT study will serve as a valuable resource for those seeking to implement commercial solutions as complementary tools to tackle air pollution. [ABSTRACT FROM AUTHOR]

This paper introduces FUME 2.0, an open-source emission processor for air quality modeling, and documents the software structure, capabilities, and sample usage. FUME provides a customizable framework for emission preparation tailored to user needs. It is designed to work with heterogeneous emission inventory data, unify them into a common structure, and generate model-ready emissions for various chemical transport models (CTMs). Key features include flexibility in input data formats, support for spatial and temporal disaggregation, chemical speciation, and integration of external models like MEGAN. FUME employs a modular Python interface and PostgreSQL/PostGIS backend for efficient data handling. The workflow comprises data import, geographical transformation, chemical and temporal disaggregation, and output generation steps. Outputs for mesoscale CTMs CMAQ, CAMx, and WRF-Chem and the large-eddy-simulation model PALM are implemented along with a generic NetCDF format. Benchmark runs are discussed on a typical configuration with cascading domains, with import and preprocessing times scaling near-linearly with grid size. FUME facilitates air quality modeling from continental to regional and urban scales by enabling effective processing of diverse inventory datasets. [ABSTRACT FROM AUTHOR]

The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas, area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower-atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UASs). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado Boulder RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 d was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. The quality-controlled TRACER-UAS observations from the CopterSonde and RAAVEN can be found at 10.5439/1969004 (Lappin, 2023) and 10.5439/1985470 (de Boer, 2023), respectively. [ABSTRACT FROM AUTHOR]

Developing air quality management systems to control the impacts of air pollution requires reliable data. However, current initiatives do not provide datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Here, we introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database of air quality and its drivers in Brazil. BRAIN encompasses hourly datasets of meteorology, emissions, and air quality. The emissions dataset includes vehicular emissions derived from the Brazilian Vehicular Emissions Inventory Software (BRAVES), industrial emissions produced with local data from the Brazilian environmental agencies, biomass burning emissions from FINN – Fire INventory from the National Center for Atmospheric Research (NCAR), and biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (10.57760/sciencedb.09858, Hoinaski et al., 2023a; 10.57760/sciencedb.09886, Hoinaski et al., 2023b). The meteorology dataset has been derived from the Weather Research and Forecasting Model (WRF) (10.57760/sciencedb.09857, Hoinaski and Will, 2023a; 10.57760/sciencedb.09885, Hoinaski and Will, 2023c). The air quality dataset contains the surface concentration of 216 air pollutants produced from coupling meteorological and emissions datasets with the Community Multiscale Air Quality Modeling System (CMAQ) (10.57760/sciencedb.09859, Hoinaski and Will, 2023b; 10.57760/sciencedb.09884, Hoinaski and Will, 2023d). We provide gridded data in two domains, one covering the Brazilian territory with 20×20 km spatial resolution and another covering southern Brazil with 4×4 km spatial resolution. This paper describes how the datasets were produced, their limitations, and their spatiotemporal features. To evaluate the quality of the database, we compare the air quality dataset with 244 air quality monitoring stations, providing the model's performance for each pollutant measured by the monitoring stations. We present a sample of the spatial variability of emissions, meteorology, and air quality in Brazil from 2019, revealing the hotspots of emissions and air pollution issues. By making BRAIN publicly available, we aim to provide the required data for developing air quality policies on municipal and state scales, especially for under-developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights into and opportunities for air pollution research in Brazil.​​​​​​​ [ABSTRACT FROM AUTHOR]

Anthropogenic emissions are the result of many different economic sectors, including transportation, power generation, industrial, residential and commercial activities, waste treatment and agricultural practices. Air quality models are used to forecast the atmospheric composition, analyze observations and reconstruct the chemical composition of the atmosphere during the previous decades. In order to drive these models, gridded emissions of all compounds need to be provided. This paper describes a new global inventory of emissions called CAMS-GLOB-ANT, developed as part of the Copernicus Atmosphere Monitoring Service (CAMS; 10.24380/eets-qd81, Soulie et al., 2023). The inventory provides monthly averages of the global emissions of 36 compounds, including the main air pollutants and greenhouse gases, at a spatial resolution of 0.1° × 0.1° in latitude and longitude, for 17 emission sectors. The methodology to generate the emissions for the 2000–2023 period is explained, and the datasets are analyzed and compared with publicly available global and regional inventories for selected world regions. Depending on the species and regions, good agreements as well as significant differences are highlighted, which can be further explained through an analysis of different sectors as shown in the figures in the Supplement. [ABSTRACT FROM AUTHOR]

This paper introduces a machine-learning-driven approach for automated Nocturnal Low-Level Jet (NLLJ) identification using observations of wind profiles from a Radar Wind Profiler (RWP). The work discussed here is an effort to lay the groundwork for a systematic study of the Mid-Atlantic NLLJ's formation mechanisms and their influence on nocturnal and diurnal air quality in major urban regions by establishing a general framework of NLLJ features and characteristics with an identification algorithm. Leveraging a comprehensive wind profile dataset maintained by the Maryland Department of Environment's RWP network, our methodology employs supervised machine learning techniques to isolate the features of the south-westerly NLLJ. This methodology was developed to illuminate spatiotemporal patterns and nuanced characteristics of NLLJ events, unveiling their significant role in shaping the planetary boundary layer. This paper discusses the construction of this methodology, its performance against known NLLJs in the current literature, intended usage, and a preliminary statistical analysis. First light results from this analysis have identified a total of 90 south-westerly NLLJs from May–September of 2017–2021 as captured by the RWP stationed in Beltsville, MD (39.05°, -76.87°, 135 m ASL). A composite of these 90 jets is presented to better illustrate many of the bulk parameters, such as core height, duration, and maximum wind speed, associated with the onset and decay of the Mid-Atlantic NLLJ. We hope our study equips researchers and policymakers with further means to monitor, predict, and address these nocturnal dynamics phenomena that frequently influence boundary layer composition and air quality in the U.S. Mid-Atlantic and Northeastern regions. [ABSTRACT FROM AUTHOR]

This paper discusses the newly developed Decision Support System version 1.0 (DSS v1.0) for air quality management activities in Delhi, India. In addition to standard air quality forecasts, DSS provides the contribution of Delhi, its surrounding districts, and stubble-burning fires in the neighboring states of Punjab and Haryana to the PM2.5 load in Delhi. DSS also quantifies the effects of local and neighborhood emission-source-level interventions on the pollution load in Delhi. The DSS-simulated Air Quality Index for the post-monsoon and winter seasons of 2021–2022 shows high accuracy (up to 80 %) and a very low false alarm ratio (∼ 20 %) from day 1 to day 5 of the forecasts, especially when the ambient air quality index (AQI) is > 300. During the post-monsoon season (winter season), emissions from Delhi, the rest of the National Capital Region (NCR)'s districts, biomass-burning activities, and all other remaining regions on average contribute 34.4 % (33.4 %), 31 % (40.2 %), 7.3 % (0.1 %), and 27.3 % (26.4 %), respectively, to the PM2.5 load in Delhi. During peak pollution events (stubble-burning periods or wintertime), however, the contribution from the main sources (farm fires in Punjab–Haryana or local sources within Delhi) could reach 65 %–69 %. According to DSS, a 20 % (40 %) reduction in anthropogenic emissions across all NCR districts would result in a 12 % (24 %) reduction in PM2.5 in Delhi on a seasonal mean basis. DSS is a critical tool for policymakers because it provides such information daily through a single simulation with a plethora of emission reduction scenarios. [ABSTRACT FROM AUTHOR]

Exposure to bioaerosol allergens such as pollen can cause exacerbations of allergenic airway disease (AAD) in sensitive populations, and thus cause serious public health problems. Assessing these health impacts by linking the airborne pollen levels, concentrations of respirable allergenic material, and human allergenic response under current and future climate conditions is a key step toward developing preventive and adaptive actions. To that end, a regional-scale pollen emission and transport modeling framework was developed that treats allergenic pollens as non-reactive tracers within the WRF/CMAQ air-quality modeling system. The Simulator of the Timing and Magnitude of Pollen Season (STaMPS) model was used to generate a daily pollen pool that can then be emitted into the atmosphere by wind. The STaMPS is driven by species-specific meteorological (temperature and/or precipitation) threshold conditions and is designed to be flexible with respect to its representation vegetation species and plant functional types (PFTs). The hourly pollen emission flux was parameterized by considering the pollen pool, friction velocity, and wind threshold values. The dry deposition velocity of each species of pollen was estimated based on pollen grain size and density. An evaluation of the pollen modeling framework was conducted for southern California for the period from March to June 2010. This period coincided with observations by the University of Southern California's Children's Health Study (CHS), which included O3, PM2.5, and pollen count, as well as measurements of exhaled nitric oxide in study participants. Two nesting domains with horizontal resolutions of 12 km and 4 km were constructed, and six representative allergenic pollen genera were included: birch tree, walnut tree, mulberry tree, olive tree, oak tree, and brome grasses. Under the current parameterization scheme, the modeling framework tends to underestimate walnut and peak oak pollen concentrations, and tends to overestimate grass pollen concentrations. The model shows reasonable agreement with observed birch, olive, and mulberry tree pollen concentrations. Sensitivity studies suggest that the estimation of the pollen pool is a major source of uncertainty for simulated pollen concentrations. Achieving agreement between emission modeling and observed pattern of pollen releases is the key for successful pollen concentration simulations. [ABSTRACT FROM AUTHOR]

The evaluation of regional air quality models is a challenging task, not only for the intrinsic complexity of the topic but also in view of the difficulties in finding sufficiently abundant, harmonized and time/space-well-distributed measurement data. This study, conducted in the framework of AQMEII (Air Quality Model Evaluation International Initiative), evaluates 4-D model predictions obtained from 15 modelling groups and relating to the air quality of the full year of 2006 over the North American and European continents. The modelled variables are ozone, CO, wind speed and direction, temperature, and relative humidity. Model evaluation is supported by the high quality in-flight measurements collected by instrumented commercial aircrafts in the context of the MOZAIC programme. The models are evaluated at five selected domains positioned around major airports, four in North America (Portland, Philadelphia, Atlanta, Dallas) and one in Europe (Frankfurt). Due to the extraordinary scale of the exercise (number of models and variables, spatial and temporal extent), this study is primarily aimed at illustrating the potential for using MOZAIC data for regional-scale evaluation and the capabilities of models to simulate concentration and meteorological fields in the vertical rather than just at the ground. We apply various approaches, metrics, and methods to analyze this complex dataset. Results of the investigation indicate that, while the observed meteorological fields are modelled with some success, modelling CO in and above the boundary layer remains a challenge and modelling ozone also has room for significant improvement. We note, however, that the high sensitivity of models to height, season, location, and metric makes the results rather difficult to interpret and to generalize. With this work, though, we set the stage for future process-oriented and in-depth diagnostic analyses. [ABSTRACT FROM AUTHOR]

The article presents information on a study that investigates the characteristics 10 of the Advanced Scatterometer (ASCAT) high-rank wind solutions under different conditions. The study aimed at developing a method for rejecting spurious high-rank solutions. It is stated that the implementation of the procedure improves the effectiveness of the ASCAT wind quality control (QC) as well as ambiguity removal procedures.

The article presents information on a study to develop and implement a laboratory prototype pulsed cavity ring-down spectroscopy (CRDS) system. The study aimed at developing the CRDS for high time resolution, continuous and automated measurement of gaseous elemental mercury (GEM) concentrations in ambient air.

The article focuses on the development of a pseudo-observation simulator to evaluate the added-value of Infrared Atmospheric Sounder Interferometer-New Generation (IASI-NG) to observation of lower tropospheric (LT) ozone. It states that LT ozone is an important factor for the evaluation of the air quality (AQ). It argues that IASI-NG helps in the better monitoring of patterns in the ozone.

Chemical transport models (CTMs), used for the prediction of, for example, nitrogen deposition or air quality changes, require estimates of the growing season of plants for a number of reasons. Typically, the growing seasons are defined in a very simplified way in CTMs, using e.g. fixed dates or simple functions. In order to explore the importance of more realistic growing season estimates, we have developed a new and simple method (the "T5" method) for calculating the start of the growing season (SGS) of birch (which we use as a surrogate for deciduous trees), suitable for use in CTMs and other modelling systems. We developed the "T5" method from observations, and here we compare with these and other methodologies, and show that with just two parameters "T5" captures well the spatial variation in SGS across Europe. We use the EMEP MSC-W chemical transport model to illustrate the importance of improved SGS estimates for ozone and two metrics associated with ozone-damage to vegetation. This study shows that although inclusion of more realistic growing seasons has only small effects on annual average concentrations of pollutants such as ozone, the metrics associated with vegetation-risk from ozone are significantly affected. This work demonstrates a strong need to include more realistic treatments of growing seasons in CTMs. The method used here could also be suitable for other types of models which require information on vegetation cover, such as meteorological and regional climate models. In future work, the "T5" and other methods will also be further evaluated for use with agricultural and grassland land-covers, which are important for emissions and deposition of reactive nitrogen compounds. [ABSTRACT FROM AUTHOR]

The article presents a study on differences in ozone production from carbon bond gas-phase chemical mechanism versions CB05 and CBMIV. It mentions the creation of the mechanisms' box model as enforced in the National Air Quality Forecasting Capability (NAQFC) modeling system as well as the development of 12 sensitivity simulations. It also notes the emulation of their chemical regime range in a continental-scale atmospheric chemistry model.

An innovative Extended Response Surface Modeling technique (ERSM v1.0) is developed to characterize the nonlinear response of fine particles (PM2.5) to large and simultaneous changes of multiple precursor emissions from multiple regions and sectors. The ERSM technique is developed starting from the conventional Response Surface Modeling (RSM) technique; it first quantifies the relationship between PM2.5 concentrations and precursor emissions in a single region with the conventional RSM technique, and then assesses the effects of inter-regional transport of PM2.5 and its precursors on PM2.5 concentrations in the target region. We apply this novel technique with a widely used regional air quality model over the Yangtze River Delta (YRD) region of China, and evaluate the response of PM2.5 and its inorganic components to the emissions of 36 pollutant-region-sector combinations. The predicted PM2.5 concentrations agree well with independent air quality model simulations; the correlation coefficients are larger than 0.98 and 0.99, and the mean normalized errors are less than 1 and 2% for January and August, respectively. It is also demonstrated that the ERSM technique could reproduce fairly well the response of PM2.5 to continuous changes of precursor emission levels between zero and 150 %. Employing this new technique, we identify the major sources contributing to PM2.5 and its inorganic components in the YRD region. The nonlinearity in the response of PM2.5 to emission changes is characterized and the underlying chemical processes are illustrated. [ABSTRACT FROM AUTHOR]

Simulations of the global dust cycle and its interactions with a changing Earth system are hindered by the empirical nature of dust emission parameterizations in climate models. Here we take a step towards improving global dust cycle simulations by presenting a physically-based dust emission model. The resulting dust flux parameterization depends only on the wind friction speed and the soil's threshold friction speed, and can therefore be readily implemented into climate models. We show that our parameterization's functional form is supported by a compilation of quality-controlled vertical dust flux measurements, and that it better reproduces these measurements than existing parameterizations. Both our theory and measurements indicate that many climate models underestimate the dust flux's sensitivity to soil erodibility. This finding can explain why dust cycle simulations in many models are improved by using an empirical preferential sources function that shifts dust emissions towards the most erodible regions. In fact, implementing our parameterization in a climate model produces even better agreement against aerosol optical depth measurements than simulations that use such a source function. These results indicate that the need to use a source function is at least partially eliminated by the additional physics accounted for by our parameterization. Since soil erodibility is affected by climate changes, our results further suggest that many models have underestimated the climate sensitivity of the global dust cycle. [ABSTRACT FROM AUTHOR]

Atmospheric chemistry plays a key role in determining the amounts and distributions of oxidants and gaseous precursors that control the formation of secondary gaseous and aerosol pollutants; all of those species can interact with the climate system. To understand the impacts of different gas-phase mechanisms on global air quality and climate predictions, in this work, a comprehensive comparative evaluation is performed using the Community Atmosphere Model (CAM) Version 5 with comprehensive tropospheric and stratospheric chemistry (CAM5-chem) within the Community Earth System Model (CESM) with two most commonly-used gas-phase chemical mechanisms: the 2005 Carbon Bond mechanism with Global Extension (CB05-GE) and the Model of OZone and Related chemical Tracers version 4 (MOZART-4) mechanism with additional updates (MOZART-4x). MOZART-4x and CB05-GE use different approaches to represent volatile organic compounds (VOCs) and different surrogates for secondary organic aerosol (SOA) precursors. MOZART-4x includes a more detailed representation of isoprene chemistry compared to CB05-GE. CB05-GE includes additional oxidation of SO2 by O3 over the surface of dust particles, which is not included in MOZART-4x. The results show that the two CAM5-chem simulations with CB05-GE and MOZART-4x predict similar chemical profiles for major gases (e.g., O3, CO, and NOx) compared to the aircraft measurements, with generally better agreement for NOy profile by CB05-GE than MOZART-4x. The concentrations of SOA at four sites in CONUS and organic carbon (OC) over the IMPROVE sites are well predicted by MOZART-4x (with NMBs of -1.9 and 2.1 %, respectively) but moderately underpredicted by CB05-GE (with NMBs of -23.1 and -20.7 %, respectively). This is mainly due to the higher biogenic emissions and hydroxyl radical levels simulated with MOZART-4x than with CB05-GE. The concentrations of OC over Europe are largely underpredicted by both MOZART-4x and CB05-GE, with NMBs of -73.0 and -75.1 %, respectively, indicating the uncertainties in the emissions of precursors and primary OC and relevant model treatments such as the oxidations of VOCs and SOA formation. Uncertainties in the emissions and convection scheme can contribute to the large bias in the model predictions (e.g., SO2, CO, black carbon, and aerosol optical depth). The two simulations also have similar cloud/radiative predictions, with slightly better performance of domain average cloud condensation nuclei (CCN) at supersaturation of 0.5% by CB05-GE, but slightly better 5 agreement with observed CCN (at supersaturation of 0.2%) profile over Beijing by MOZART-4x. The two gas-phase mechanisms result in a global average difference of 0.5Wm-2 in simulated shortwave cloud radiative forcing, with significant differences (e.g., up to 13.6Wm-2) over subtropical regions [ABSTRACT FROM AUTHOR]

In this study we develop a Secondary Inorganic Aerosol (SIA) module for the chemistry transport model MOCAGE developed at CNRM. Based on the thermodynamic equilibrium module ISORROPIA II, the new version of the model is evaluated both at the global scale and at the regional scale. The results show high concentrations of secondary inorganic aerosols in the most polluted regions being Europe, Asia and the eastern part of North America. Asia shows higher sulfate concentrations than other regions thanks to emissions reduction in Europe and North America. Using two simulations, one with and the other without secondary inorganic aerosol formation, the model global outputs are compared to previous studies, to MODIS AOD retrievals, and also to in situ measurements from the HTAP database. The model shows a better agreement in all geographical regions with MODIS AOD retrievals when introducing SIA. It also provides a good statistical agreement with in situ measurements of secondary inorganic aerosol composition: sulfate, nitrate and ammonium. In addition, the simulation with SIA gives generally a better agreement for secondary inorganic aerosols precursors (nitric acid, sulfur dioxide, ammonia) in particular with a reduction of the Modified Normalised Mean Bias (MNMB). At the regional scale, over Europe, the model simulation with SIA are compared to the in situ measurements from the EMEP database and shows a good agreement with secondary inorganic aerosol composition. The results at the regional scale are consistent with those obtained with the global simulations. The AIRBASE database was used to compare the model to regulated air quality pollutants being particulate matter, ozone and nitrogen dioxide concentrations. The introduction of the SIA in MOCAGE provides a reduction of the PM2.5 MNMB of 0.44 on a yearly basis and even 0.52 on a three spring months period (March, April, May) when SIA are maximum. [ABSTRACT FROM AUTHOR]

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transformation, transport and fate of the many different air pollutant species that comprise particulate matter (PM), including dust (or soil). The CMAQ model version 5.0 (CMAQv5.0) has several enhancements over the previous version of the model for estimating the emission and transport of dust, including the ability to track the specific elemental constituents of dust and have the modelderived concentrations of those elements participate in chemistry. The latest version of the model also includes a parameterization to estimate emissions of dust due to wind action. The CMAQv5.0 modeling system was used to simulate the entire year 2006 for the continental United States, and the model estimates were evaluated against daily surface based measurements from several air quality networks. The CMAQ modeling system generally did well replicating the observed soil concentrations in the western United States; however the model consistently overestimated the observed soil con centrations in the eastern United States, regardless of season. The performance of the individual trace metals was generally good at the rural network sites, with relatively small biases for Fe, Al, Si and Ti throughout the year, while Ca, K and Mn were overestimated and Mg underestimated. For the urban sites, Fe, Mg and Mn, while overestimated, had comparatively better performance throughout the year than the other trace metals, which were consistently overestimated, including very large overestimations of Al, Ti and Si in the fall. An underestimation of nighttime mixing in the urban areas appears to contribute to the overestimation of trace metals. Removing the anthropogenic fugitive dust (AFD) emissions and the effects of wind-blown dust (WBD) lowered the model soil concentrations. However, even with both AFD emissions and WBD effects removed, soil concentrations were still often overestimated, suggesting that there are other sources of errors in the modeling system that contribute to the overestimation of soil components. Efforts are underway to improve both the nighttime mixing in urban areas and the spatial and temporal distribution of dust related emissions sources in the emissions inventory. [ABSTRACT FROM AUTHOR]

The article discusses a research study which introduces the Market Allocation (MARKAL) method for anthropogenic emission scenarios. The said approach focuses on energy use and production because these factors are the main sources of air pollution and uses two emission scenarios. Findings reveal that both scenarios demonstrate significant reductions in air pollutant emissions.

The article offers information on the modification of the U.S. Environmental Protection Agency (EPA) regional emission model known as SMOKE for Europe (SMOKE-EU). It mentions that SMOKE-EU is capable of producing Community Multiscale Air Quality (CMAQ) ready emissions not only in Europe but also in Western Russia, Turkey and North Africa. The purpose of the modification is to develop a flexible model capable of producing high resolution emission data for Chemical Transport Models (CTM).

The significance of air quality monitoring for analyzing impact on public health is growing worldwide. A crucial part of smart city development includes deployment of suitable air pollution sensors at critical locations. Note that there are various air quality measurement instruments, ranging from expensive reference stations that provide accurate data to low-cost sensors that provide less accurate air quality measurements. In this research, we use a combination of sensors and monitors, which we call hybrid instruments, and focus on optimal placement of such instruments across a region. The objective of the problem is to maximize a satisfaction function that quantifies the weighted closeness of different regions to the places where such hybrid instruments are placed (here weights for different regions are quantified in terms of the relative population density and relative PM 2.5 concentration). Note that there can be several constraints such as those on budget, the minimum number of reference stations to be placed, or the set of important regions where at least one sensor should be placed. We develop two algorithms to solve this problem. The first one is a genetic algorithm that is a metaheuristic and that works on the principles of evolution. The second one is a greedy algorithm that selects the locally best choice in each iteration. We test these algorithms on different regions from India with varying sizes and other characteristics such as population distribution, PM 2.5 emissions, or available budget. The insights obtained from this paper can be used to quantitatively place reference stations and sensors in large cities rather than using ad hoc procedures or rules of thumb. [ABSTRACT FROM AUTHOR]

As air pollution is regarded as the single largest environmental health risk in Europe it is important that communication to the public is up to date and accurate and provides means to avoid exposure to high air pollution levels. Long- and short-term exposure to outdoor air pollution is associated with increased risks of mortality and morbidity. Up-to-date information on present and coming days' air quality helps people avoid exposure during episodes with high levels of air pollution. Air quality forecasts can be based on deterministic dispersion modelling, but to be accurate this requires detailed information on future emissions, meteorological conditions and process-oriented dispersion modelling. In this paper, we apply different machine learning (ML) algorithms – random forest (RF), extreme gradient boosting (XGB), and long short-term memory (LSTM) – to improve 1, 2, and 3 d deterministic forecasts of PM 10 , NOx , and O 3 at different sites in Greater Stockholm, Sweden. It is shown that the deterministic forecasts can be significantly improved using the ML models but that the degree of improvement of the deterministic forecasts depends more on pollutant and site than on what ML algorithm is applied. Also, four feature importance methods, namely the mean decrease in impurity (MDI) method, permutation method, gradient-based method, and Shapley additive explanations (SHAP) method, are utilized to identify significant features that are common and robust across all models and methods for a pollutant. Deterministic forecasts of PM 10 are improved by the ML models through the input of lagged measurements and Julian day partly reflecting seasonal variations not properly parameterized in the deterministic forecasts. A systematic discrepancy by the deterministic forecasts in the diurnal cycle of NOx is removed by the ML models considering lagged measurements and calendar data like hour and weekday, reflecting the influence of local traffic emissions. For O 3 at the urban background site, the local photochemistry is not properly accounted for by the relatively coarse Copernicus Atmosphere Monitoring Service ensemble model (CAMS) used here for forecasting O 3 but is compensated for using the ML models by taking lagged measurements into account. Through multiple repetitions of the training process, the resulting ML models achieved improvements for all sites and pollutants. For NOx at street canyon sites, mean squared error (MSE) decreased by up to 60 %, and seven metrics, such as R2 and mean absolute percentage error (MAPE), exhibited consistent results. The prediction of PM 10 is improved significantly at the urban background site, whereas the ML models at street sites have difficulty capturing more information. The prediction accuracy of O 3 also modestly increased, with differences between metrics. Further work is needed to reduce deviations between model results and measurements for short periods with relatively high concentrations (peaks) at the street canyon sites. Such peaks can be due to a combination of non-typical emissions and unfavourable meteorological conditions, which are rather difficult to forecast. Furthermore, we show that general models trained using data from selected street sites can improve the deterministic forecasts of NOx at the station not involved in model training. For PM 10 this was only possible using more complex LSTM models. An important aspect to consider when choosing ML algorithms is the computational requirements for training the models in the deployment of the system. Tree-based models (RF and XGB) require fewer computational resources and yield comparable performance in comparison to LSTM. Therefore, tree-based models are now implemented operationally in the forecasts of air pollution and health risks in Stockholm. Nevertheless, there is big potential to develop generic models using advanced ML to take into account not only local temporal variation but also spatial variation at different stations. [ABSTRACT FROM AUTHOR]

From the ensemble of stations that monitor surface air quality over the United States and Europe, we identify extreme ozone pollution events and find that they occur predominantly in clustered, multi-day episodes with spatial extents of more than 1000 km. Such scales are amenable to forecasting with current global atmospheric chemistry models. We develop an objective mapping algorithm that uses the heterogeneous observations of the individual surface sites to calculate surface ozone averaged over 1° by 1° grid cells, matching the resolution of a global model. Air quality extreme (AQX) events are identified locally as statistical extremes of the ozone climatology and not as air quality exceedances. With the University of California, Irvine chemistry-transport model (CTM) we find there is skill in hindcasting these extreme episodes, and thus identify a new diagnostic using global chemistry-climate models (CCM) to identify changes in the characteristics of extreme pollution episodes in a warming climate. [ABSTRACT FROM AUTHOR]

Emissions to air are reported by countries to EMEP. The emissions data are used for country compliance checking with EU emission ceilings and associated emission reductions. The emissions data are also necessary as input for air quality modelling. The quality of these "official" emissions varies across Europe. As alternative to these official emissions, a spatially explicit high resolution emission inventory (7km×7 km) for UNECE-Europe for all years between 2003 and 2009 for the main air pollutants was made. The primary goal was to supply air quality modellers with the input they need. The inventory was constructed by using the reported emission national totals by sector where the quality is sufficient. The reported data were analysed by sector in detail, and completed with alternative emission estimates as needed. This resulted in a complete emission inventory for all countries. For particulate matter, for each source emissions have been split in coarse and fine particulate matter, and further disaggregated to EC, OC, SO4, Na and others using fractions based on literature. Doing this at the most detailed sectoral level in the database implies that a consistent set was obtained across Europe. This allows better comparisons with observational data which can, through feedback, help to further identify uncertain sources and/or support emission inventory improvements for this highly uncertain pollutant. The resulting emission dataset was spatially distributed consistently across all countries by using proxy parameters. Point sources were spatially distributed using the specific location of the point source. The spatial distribution for the point sources was made year-specific. The TNO-MACC_II is an update of the TNO-MACC emission dataset. Major updates included the time extension towards 2009, use of the latest available reported data (including updates and corrections made until early 2012) and updates in distribution maps. [ABSTRACT FROM AUTHOR]

Vehicular emissions are one of the most important sources of pollution in China, and they can increase the ambient concentrations of air pollutants and degrade the air quality. Using data released by the National Bureau of Statistics, vehicular emissions in China in 2006 and 2010 were calculated at a high spatial resolution, by taking the emission standards into consideration. The results show that China's vehicular emissions of CO, NOx, VOCs, NH3, PM2.5, PM10, black carbon (BC), and organic carbon (OC) were 30113.9, 4593.7, 6838.0, 20.9, 400.2, 430.5, 285.6, and 105.1 Gg, respectively, in 2006 and 34175.2, 5167.5, 7029.4, 74.0, 386.4, 417.1, 270.9, and 106.2 Gg, respectively, in 2010. CO, VOCs, and NH3 emissions were mainly from motorcycles and light-duty gasoline vehicles, whereas NOx, PM2.5, PM10, and BC emissions were mainly from rural vehicles and heavy-duty diesel trucks. OC emissions were mainly from motorcycles and heavy-duty diesel trucks. Euro 0 and Euro I vehicles were the primary contributors to all of the pollutant emissions except NH3, which was mainly from Euro III and Euro IV vehicles. The spatial distribution of vehicular emissions in China in 2006 and 2010 were developed at a high resolution of 0.25° × 0.25°, by using the road traffic density to characterize the busyness of a road. This method could overcome the problem of getting traffic flow information and make the spatial allocation more closed to the actual road emissions. The results showed that vehicular emissions presented significant regional spatial distribution, and emissions in the eastern and southern parts of China were much higher than those in western and northern China in both years. The North China Plain, Yangtze River Delta, and Pearl River Delta regions jointly accounted for nearly half of the emissions. NH3 emissions increased greatly in big cities from 2006 to 2010. Emissions of CO, NOx, and VOCs could increase 52%, 9%, and 68%, if the emission standard and oil quality remained in the Euro I stage, so the policies on vehicular emissions implemented in China were demonstrated to be effective. Nevertheless, greater efforts are needed to improve the oil quality so that the new emission standard can implement timely and catch the international level quickly, especially the sulfur level because NOx emission is very sensitive to it. By comparing with coal consumption and NO2 column density observed by SCIA satellite, the increase of NOx emission in China from 2006 to 2010 was mainly caused by coal consumption. [ABSTRACT FROM AUTHOR]

Ozone, PM10 and PM2.5 concentrations over Paris, France and Stockholm, Sweden were modeled at 4 and 1 km horizontal resolutions respectively for the present and 2050 periods employing decade-long simulations.We account for large-scale global climate change (RCP-4.5) and fine resolution bottom-up emission projections developed by local experts and quantify their impact on future pollutant concentrations. Moreover, we identify biases related to the implementation of regional scale emission projections over the study areas by comparing modeled pollutant concentrations between the fine and coarse scale simulations. We show that over urban areas with major regional contribution (e.g., the city of Stockholm) the bias due to coarse emission inventory may be significant and lead to policy misclassification. Our results stress the need to better understand the mechanism of bias propagation across the modeling scales in order to design more successful local-scale strategies. We find that the impact of climate change is spatially homogeneous in both regions, implying strong regional influence. The climate benefit for ozone (daily average and maximum) is up to -5% for Paris and -2% for Stockholm city. The joined climate benefit on PM2.5 and PM10 in Paris is between -10 and -5% while for Stockholm we observe mixed trends up to 3% depending on season and size class. In Stockholm, emission mitigation leads to concentration reductions up to 15% for daily average and maximum ozone and 20% for PM and through a sensitivity analysis we show that this response is entirely due to changes in emissions at the regional scale. On the contrary, over the city of Paris (VOC-limited photochemical regime), local mitigation of NOx emissions increases future ozone concentrations due to ozone titration inhibition. This competing trend between the respective roles of emission and climate change, results in an increase in 2050 daily average ozone by 2.5% in Paris. Climate and not emission change appears to be the most influential factor for maximum ozone concentration over the city of Paris, which may be particularly interesting in a health impact perspective. [ABSTRACT FROM AUTHOR]

The negative impacts of fine particulate matter (PM2.5) exposure on human health are a primary motivator for air quality research. However, estimates of the air pollution health burden vary considerably and strongly depend on the datasets and methodology. Satellite observations of aerosol optical depth (AOD) have been widely used to overcome limited coverage from surface monitoring and to assess the global population exposure to PM2.5 and the associated premature mortality. Here we quantify the uncertainty in determining the burden of disease using this approach, discuss different methods and datasets, and explain sources of discrepancies among values in the literature. For this purpose we primarily use the MODIS satellite observations in concert with the GEOS-Chem chemical transport model. We contrast results in the United States and China for the years 2004-2011. We estimate that in the United States, exposure to PM2.5 accounts for approximately 4 % of total deaths compared to 22 % in China (using satellite-based exposure), which falls within the range of previous estimates. The difference in estimated mortality burden based solely on a global model vs. that derived from satellite is approximately 9 % for the US and 4 % for China on a nationwide basis, although regionally the differences can be much greater. This difference is overshadowed by the uncertainty in the methodology for deriving PM2.5 burden from satellite observations, which we quantify to be on order of 20 % due to uncertainties in the AOD-to-surface-PM2.5 relationship, 10 % due to the satellite observational uncertainty, and 30 % or greater uncertainty associated with the application of concentration response functions to estimated exposure. [ABSTRACT FROM AUTHOR]