LUCC-induced dust aerosol change increase surface and reduce atmospheric direct radiative forcing in Northern China

Land use and land cover change (LUCC) can alter surface properties, such as albedo, roughness, and vegetation coverage, directly affecting dust emissions and aerosol concentrations, leading to variations in direct radiative forcing (DRF) of dust aerosols and consequently impacting the climate. This study utilized the Weather Research and Forecasting model with Chemistry (WRF-Chem) to quantify the impact of LUCC in northern China from 2000 to 2020 on dust aerosol DRF. Results indicated that LUCC's influence on shortwave radiative forcing of dust was significantly greater than its influence on longwave radiative forcing and exhibited obvious seasonal variations. Overall, LUCC can cause net direct radiative forcing to increase by 5.3 W m-2 at the surface and decrease by 7.8 W m-2 in the atmosphere. Different types of LUCC transformation showed distinct impacts on dust aerosol DRF, with the conversion from sparse vegetation to barren land had the most significant effect on net radiative intensity, resulting in a decrease of 8.1 W m-2 at the surface, an increase of 12.2 W m-2 in the atmosphere, and an increase of 4.1 W m-2 at the top of the atmosphere. Conversely, the conversion from barren land to sparse vegetation led to surface cooling and atmospheric warming. These findings are of great significance for enhancing our knowledge of the effects of LUCC on the radiative balance of dust aerosols.

Primary Sulfate Is the Dominant Source of Particulate Sulfate during Winter in Fairbanks, Alaska

Within and surrounding high-latitude cities, poor air quality disturbs Arctic ecosystems, influences the climate, and harms human health. The Fairbanks North Star Borough has wintertime particulate matter (PM) concentrations that exceed the Environmental Protection Agency's (EPA) threshold for public health. Particulate sulfate (SO4 2-) is the most abundant inorganic species and contributes approximately 20% of the total PM mass in Fairbanks, but air quality models underestimate observed sulfate concentrations. Here we quantify sulfate sources using size-resolved δ34S(SO4 2-), δ18O(SO4 2-), and Δ17O(SO4 2-) of particulate sulfate in Fairbanks from January 18th to February 25th, 2022 using a Bayesian isotope mixing model. Primary sulfate contributes 62 ± 12% of the total sulfate mass on average. Most primary sulfate is found in the size bin with a particle diameter < 0.7 μm, which contains 90 ±5% of total sulfate mass and poses the greatest risk to human health. Oxidation by all secondary formation pathways combined contributes 38 ± 12% of total sulfate mass on average, indicating that secondary sulfate formation is inefficient in this cold, dark environment. On average, the dominant secondary sulfate formation pathways are oxidation by H2O2 (13 ± 6%), O3 (8 ± 4%), and NO2 (8 ± 3%). These findings will inform mitigation strategies to improve air quality and public health in Fairbanks and possibly other high-latitude urban areas during winter.

Air quality modeling in the metropolitan area of São Paulo, Brazil: A review

Numerous studies have used air quality models to estimate pollutant concentrations in the Metropolitan Area of São Paulo (MASP) by using different inputs and assumptions. Our objectives are to summarize these studies, compare their performance, configurations, and inputs, and recommend areas of further research. We examined 29 air quality modeling studies that focused on ozone (O3) and fine particulate matter (PM2.5) performed over the MASP, published from 2001 to 2023. The California Institute of Technology airshed model (CIT) was the most used offline model, while the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was the most used online model. Because the main source of air pollution in the MASP is the vehicular fleet, it is commonly used as the only anthropogenic input emissions. Simulation periods were typically the end of winter and during spring, seasons with higher O3 and PM2.5 concentrations. Model performance for hourly ozone is good with half of the studies with Pearson correlation above 0.6 and root mean square error (RMSE) ranging from 7.7 to 27.1 ppb. Fewer studies modeled PM2.5 and their performance is not as good as ozone estimates. Lack of information on emission sources, pollutant measurements, and urban meteorology parameters is the main limitation to perform air quality modeling. Nevertheless, researchers have used measurement campaign data to update emission factors, estimate temporal emission profiles, and estimate volatile organic compounds (VOCs) and aerosol speciation. They also tested different emission spatial disaggregation approaches and transitioned to global meteorological reanalysis with a higher spatial resolution. Areas of research to explore are further evaluation of models' physics and chemical configurations, the impact of climate change on air quality, the use of satellite data, data assimilation techniques, and using model results in health impact studies. This work provides an overview of advancements in air quality modeling within the MASP and offers practical approaches for modeling air quality in other South American cities with limited data, particularly those heavily impacted by vehicle emissions.

Evaluation of Uncertainties in the Anthropogenic SO2 Emissions in the USA from the OMI Point Source Catalog

Satellite remote sensing is a promising method of monitoring emissions that may be missing in inventories, but the accuracy of these estimates is often not clear. We demonstrate here a comprehensive evaluation of errors in anthropogenic sulfur dioxide (SO₂) emission estimates from NASA's OMI point source catalog for the contiguous US by comparing emissions from the catalog with high-quality emission inventory data over different dimensions including size of individual sources, aggregate vs individual source errors, and potential bias in individual source estimates over time. For sources that are included in the catalog, we find that errors in aggregate (sum of error for all included sources) are relatively low. Errors for individual sources in any given year can be substantial, however, with over- or underestimates in terms of total error ranging from -80 to 110 kt (roughly 10-90th percentile). We find that these errors are not necessarily random over time and that there can be consistently positive or negative biases for individual sources. We did not find any overall statistical relationship between the degree of isolation of a source and bias, either at a 40 or 70 km scales. For a sub-set of sources where inventory emissions over a radius of 70 km around an OMI detection are larger than twice the emissions within 40 km, the OMI value is consistently overestimated. We find, as expected, that emission sources not included in the catalog are the largest aggregate source of difference between the satellite estimates and inventories, especially in more recent years where source emission magnitudes have been decreasing and note that trends in satellite detections do not necessarily track trends in total emissions. We find that the OMI-based SO₂ emissions are accurate in aggregate, when summed over a number of sources, but must be interpreted more cautiously at the individual source level. Similar analyses would be valuable for other satellite emission estimates; however, in many cases, the appropriate high-quality reference data may need to be generated.

A 1-D model to retrieve the vertical profiles of minor atmospheric constituents for cloud microphysical modeling: I. Formulation and validation

Determining the number concentration of minor constituents in the atmosphere is very important as it determines the whole tropospheric chemistry process. These constituents may act as cloud condensation nuclei (CCN) and ice nuclei (IN), impacting heterogeneous nucleation inside the cloud. However, the estimations of the number concentration of CCN/IN in cloud microphysical parameters are associated with uncertainties. In the present work, a hybrid Monte Carlo Gear solver has been developed to retrieve profiles of CH₄, N₂O, and SO₂. The idealized experiments have been carried out using this solver for retrieving vertical profiles of these constituents over four megacities, viz., Delhi, Mumbai, Chennai, and Kolkata. Community Long-term Infrared Microwave Coupled Atmospheric Product System (CLIMCAPS) dataset around 0800 UTC (2000UTC) has been used for initializing the number concentration of CH₄, N₂O, and SO₂ for daytime (nighttime). The daytime (nighttime) retrieved profiles have been validated using 2000 UTC (next day 0800 UTC) CLIMCAPS products. ERA5 temperature dataset has been used to estimate the kinematic rate of reactions with 1000 perturbations determined using Maximum Likelihood Estimation (MLE). The retrieved profiles and CLIMCAPS products are in very good agreement, as evidenced by the percentage difference between them within the range of 1.3 × 10^-5-60.8 % and the coefficient of determination mainly within the range between 81 and 97 %. However, during the passage of tropical cyclone and western disturbance, its value became as low as 27 and 65 % over Chennai and Kolkata, respectively. The enactment of synoptic scale systems such as western disturbances, tropical cyclone Amphan, and easterly waves caused disturbed weather over these megacities-the retrieved profiles during disturbed weather cause large deviations of vertical profiles of N₂O. However, the profiles of CH₄ and SO₂ have less deviation. It is inferred that incorporating this methodology in the dynamical model will be useful to simulate the realistic vertical profiles of the minor constituents in the atmosphere.

Exposure Assessment of Ambient PM2.5 Levels during a Sequence of Dust Episodes: A Case Study Coupling the WRF-Chem Model with GIS-Based Postprocessing

A sequence of dust intrusions occurred from the Sahara Desert to the central Mediterranean in the second half of June 2021. This event was simulated by means of the Weather Research and Forecasting coupled with chemistry (WRF-Chem) regional chemical transport model (CTM). The population exposure to the dust surface PM2.5 was evaluated with the open-source quantum geographical information system (QGIS) by combining the output of the CTM with the resident population map of Italy. WRF-Chem analyses were compared with spaceborne aerosol observations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and, for the PM2.5 surface dust concentration, with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Considering the full-period (17-24 June) and area-averaged statistics, the WRF-Chem simulations showed a general underestimation for both the aerosol optical depth (AOD) and the PM2.5 surface dust concentration. The comparison of exposure classes calculated for Italy and its macro-regions showed that the dust sequence exposure varies with the location and entity of the resident population amount. The lowest exposure class (up to 5 µg m-3) had the highest percentage (38%) of the population of Italy and most of the population of north Italy, whereas more than a half of the population of central, south and insular Italy had been exposed to dust PM2.5 in the range of 15-25 µg m-3. The coupling of the WRF-Chem model with QGIS is a promising tool for the management of risks posed by extreme pollution and/or severe meteorological events. Specifically, the present methodology can also be applied for operational dust forecasting purposes, to deliver safety alarm messages to areas with the most exposed population.

Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16

A new dynamical core, known as the Finite-Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA's Global Forecast System (GFS) and in limited-area models for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), which includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecasting Capability (NAQFC) that will continue to protect human and ecosystem health in the US. Here we describe the development of the FV3GFSv16 coupling with a "state-of-the-science" CMAQ model version 5.3.1. The GFS-CMAQ coupling is made possible by the seminal version of the NOAA-EPA Atmosphere-Chemistry Coupler (NACC), which became a major piece of the next operational NAQFC system (i.e., NACC-CMAQ) on 20 July 2021. NACC-CMAQ has a number of scientific advancements that include satellite-based data acquisition technology to improve land cover and soil characteristics and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM_2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ model has significantly different meteorological and chemical predictions compared to the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM_2.5 predictions and diurnal patterns, both of which are extended to a 72 h (3 d) forecast with this system.

CO Fluxes in Western Europe during 2017–2020 Winter Seasons Inverted by WRF-Chem/Data Assimilation Research Testbed with MOPITT Observations

The study of anthropogenic carbon monoxide (CO) emissions is crucial to investigate anthropogenic activities. Assuming the anthropogenic CO emissions accounted for the super majority of the winter CO fluxes in western Europe, they could be roughly estimated by the inversion approach. The CO fluxes and concentrations of four consecutive winter seasons (i.e., December–February) in western Europe since 2017 were estimated by a regional CO flux inversion system based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Data Assimilation Research Testbed (DART). The CO retrievals from the Measurements Of Pollution In The Troposphere instrument (MOPITT) version 8 level 2 multi-spectral Thermal InfraRed (TIR)/Near-InfraRed (NIR) CO retrieval data products were assimilated by the inversion system. The analyses of the MOPITT data used by the inversion system indicated that the mean averaging kernel row sums of the surface level was about 0.25, and the difference percentage of the surface-level retrievals relative to a priori CO-mixing ratios was 14.79%, which was similar to that of the other levels. These results suggested the MOPITT’s surface-level observations contained roughly the same amount of information as the other levels. The inverted CO fluxes of the four winter seasons were 6198.15 kilotons, 4939.72 kilotons, 4697.80 kilotons, and 5456.19 kilotons, respectively. Based on the assumption, the United Nations Framework Convention on Climate Change (UNFCCC) inventories were used to evaluate the accuracy of the inverted CO fluxes. The evaluation results indicated that the differences between the inverted CO fluxes and UNFCCC inventories of the three winter seasons of 2017–2019 were 13.36%, −4.59%, and −4.76%, respectively. Detailed surface-CO concentrations and XCO comparative analyses between the experimental results and the external Community Atmosphere Model with Chemistry (CAM-Chem) results and the MOPITT data were conducted. The comparative analysis results indicated that the experimental results of the winter season of 2017 were obviously affected by high boundary conditions. The CO concentrations results of the experiments were also evaluated by the CO observation data from Integrated Carbon Observation System (ICOS), the average Mean Bias Error (MBE), and the Root Mean Square Error (RMSE) between the CO concentrations results of the inversion system, and the ICOS observations were −22.43 ppb and 57.59 ppb, respectively. The MBE and RMSE of the inversion system were 17.53-ppb and 4.17-ppb better than those of the simulation-only parallel experiments, respectively.

Legislative and functional aspects of different metrics used for ozone risk assessment to forests

Surface ozone (O₃) is a threat to forests by decreasing photosynthesis and, consequently, influencing the strength of land carbon sink. However, due to the lack of continuous surface O₃ measurements, observational-based assessments of O₃ impacts on forests are largely missing at hemispheric to global scales. Currently, some metrics are used for regulatory purposes by governments or national agencies to protect forests against the negative impacts of ozone: in particular, both Europe and United States (US) makes use of two different exposure-based metrics, i.e. AOT40 and W126, respectively. However, because of some limitations in these metrics, a new standard is under consideration by the European Union (EU) to replace the current exposure metric. We analyse here the different air quality standards set or proposed for use in Europe and in the US to protect forests from O₃ and to evaluate their spatial and temporal consistency while assessing their effectiveness in protecting northern-hemisphere forests. Then, we compare their results with the information obtained from a complex land surface model (ORCHIDEE). We find that present O₃ uptake decreases gross primary production (GPP) in 37.7% of the NH forested area of northern hemisphere with a mean loss of 2.4% year^-1. We show how the proposed US (W126) and the currently used European (AOT40) air quality standards substantially overestimate the extension of potential vulnerable regions, predicting that 46% and 61% of the Northern Hemisphere (NH) forested area are at risk of O₃ pollution. Conversely, the new proposed European standard (POD1) identifies lower extension of vulnerability regions (39.6%).

First Chinese ultraviolet-visible hyperspectral satellite instrument implicating global air quality during the COVID-19 pandemic in early 2020

In response to the COVID-19 pandemic, governments worldwide imposed lockdown measures in early 2020, resulting in notable reductions in air pollutant emissions. The changes in air quality during the pandemic have been investigated in numerous studies via satellite observations. Nevertheless, no relevant research has been gathered using Chinese satellite instruments, because the poor spectral quality makes it extremely difficult to retrieve data from the spectra of the Environmental Trace Gases Monitoring Instrument (EMI), the first Chinese satellite-based ultraviolet-visible spectrometer monitoring air pollutants. However, through a series of remote sensing algorithm optimizations from spectral calibration to retrieval, we successfully retrieved global gaseous pollutants, such as nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO), from EMI during the pandemic. The abrupt drop in NO2 successfully captured the time for each city when effective measures were implemented to prevent the spread of the pandemic, for example, in January 2020 in Chinese cities, February in Seoul, and March in Tokyo and various cities across Europe and America. Furthermore, significant decreases in HCHO in Wuhan, Shanghai, Guangzhou, and Seoul indicated that the majority of volatile organic compounds (VOCs) emissions were anthropogenic. Contrastingly, the lack of evident reduction in Beijing and New Delhi suggested dominant natural sources of VOCs. By comparing the relative variation of NO2 to gross domestic product (GDP), we found that the COVID-19 pandemic had more influence on the secondary industry in China, while on the primary and tertiary industries in Korea and the countries across Europe and America.

The Influence of Aerosols on Satellite Infrared Radiance Simulations and Jacobians: Numerical Experiments of CRTM and GSI

For a variational data assimilation (DA) system that assimilates radiance observations, the simulated brightness temperature (BT) at the top of the atmosphere and the corresponding Jacobians carried out by the radiance observation operator are needed information. Previous studies reported that the incorporation of aerosol information into the radiance observation operator leads to cooler simulated infrared (IR) BTs and warmer temperature analyses at low levels of the atmosphere. However, the role of the aerosol-affected Jacobians in the DA system, which not only affect the determination of analysis increments but also influence the quality control and the bias correction algorithm, is yet to be investigated. This study examines the aerosol impacts on the sensitivity of IR radiance simulations, Jacobians, and the analysis increments by conducting two experiments: (i) sensitivity tests to assess how the different aspects of the aerosol profiles (i.e., mass loading, peak aerosol level, aerosol thickness layer, and bin partition) affect the simulated BT and the Jacobians from the Community Radiative Transfer Model (CRTM), which is the radiance observation operator in the Gridpoint Statistical Interpolation (GSI) analysis system; (ii) single IR observation experiments using GSI to investigate how the aerosol-affected atmospheric Jacobians influence the analysis increment. The results show that dust aerosols produce the strongest cooling to simulated BTs under similar aerosol optical depths; simulated BTs and Jacobians are most sensitive to the loading and peak altitude of the aerosol layer; simulated BTs become more sensitive to the temperature of the aerosol layer; aerosol-induced differences in atmospheric Jacobians lead to considerable changes to temperature and moisture increments. These results provide a better understanding of the aerosol impacts on each component involved in radiance DA, which can provide guidance for assimilating aerosol-affected IR observations.

Sector-Based Top-Down Estimates of NO x , SO2, and CO Emissions in East Asia

Top-down estimates using satellite data provide important information on the sources of air pollutants. We develop a sector-based 4D-Var framework based on the GEOS-Chem adjoint model to address the impacts of co-emissions and chemical interactions on top-down emission estimates. We apply OMI NO₂, OMI SO₂, and MOPITT CO observations to estimate NO _x , SO₂, and CO emissions in East Asia during 2005-2012. Posterior evaluations with surface measurements show reduced normalized mean bias (NMB) by 7% (NO₂)-15% (SO₂) and normalized mean square error (NMSE) by 8% (SO₂)-9% (NO₂) compared to a species-based inversion. This new inversion captures the peak years of Chinese SO₂ (2007) and NO _x (2011) emissions and attributes their drivers to industry and energy activities. The CO peak in 2007 in China is driven by residential and industry emissions. In India, the inversion attributes NO _x and SO₂ trends mostly to energy and CO trend to residential emissions.

An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data

Prior knowledge of the effectiveness of new observation instruments or new data streams for air quality can contribute significantly to shaping the policy and budget planning related to those instruments and data. In view of this, one of the main purposes of the development and application of the Observing System Simulation Experiments (OSSE) is to assess the potential impact of new observations on the quality of the current monitoring or forecasting systems, thereby making this framework valuable. This study introduces the overall OSSE framework established to support air quality forecasting and the details of its individual components. Furthermore, it shows case study results from Northeast Asia and the potential benefits of the new observation data scenarios on the PM2.5 forecasting skills, including the PM data from 200 virtual monitoring sites in the Gobi Desert and North Korean non-forest areas (NEWPM) and the aerosol optical depths (AOD) data from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS AOD). Performance statistics suggest that the concurrent assimilation of the NEWPM and the PM data from current monitoring sites in China and South Korea can improve the PM2.5 concentration forecasts in South Korea by 66.4% on average for October 2017 and 95.1% on average for February 2018. Assimilating the GEMS AOD improved the performance of the PM2.5 forecasts in South Korea for October 2017 by approximately 68.4% (~78.9% for February 2018). This OSSE framework is expected to be continuously implemented to verify its utilization potential for various air quality observation systems and data scenarios. Hopefully, this kind of application result will aid environmental researchers and decision-makers in performing additional in-depth studies for the improvement of PM air quality forecasts.

Large Air Quality and Public Health Impacts due to Amazonian Deforestation Fires in 2019

Air pollution from Amazon fires has adverse impacts on human health. The number of fires in the Amazon has increased in recent years, but whether this increase was driven by deforestation or climate has not been assessed. We analyzed relationships between fire, deforestation, and climate for the period 2003 to 2019 among selected states across the Brazilian Legal Amazon (BLA). A statistical model including deforestation, precipitation and temperature explained ∼80% of the variability in dry season fire count across states when totaled across the BLA, with positive relationships between fire count and deforestation. We estimate that the increase in deforestation since 2012 increased the dry season fire count in 2019 by 39%. Using a regional chemistry-climate model combined with exposure-response associations, we estimate this increase in fire resulted in 3,400 (95UI: 3,300-3,550) additional deaths in 2019 due to increased exposure to particulate air pollution. If deforestation in 2019 had increased to the maximum recorded during 2003-2019, the number of active fire counts would have increased by an additional factor of 2 resulting in 7,900 (95UI: 7,600-8,200) additional premature deaths. Our analysis demonstrates the strong benefits of reduced deforestation on air quality and public health across the Amazon.

Recommendations for HCHO and SO2 Retrieval Settings from MAX-DOAS Observations under Different Meteorological Conditions

Recently, the occurrence of fog and haze over China has increased. The retrieval of trace gases from the multi-axis differential optical absorption spectroscopy (MAX-DOAS) is challenging under these conditions. In this study, various reported retrieval settings for formaldehyde (HCHO) and sulfur dioxide (SO2) are compared to evaluate the performance of these settings under different meteorological conditions (clear day, haze, and fog). The dataset from 1st December 2019 to 31st March 2020 over Nanjing, China, is used in this study. The results indicated that for HCHO, the optimal settings were in the 324.5–359 nm wavelength window with a polynomial order of five. At these settings, the fitting and root mean squared (RMS) errors for column density were considerably improved for haze and fog conditions, and the differential slant column densities (DSCDs) showed more accurate values compared to the DSCDs between 336.5 and 359 nm. For SO2, the optimal settings for retrieval were found to be at 307–328 nm with a polynomial order of five. Here, root mean square (RMS) and fitting errors were significantly lower under all conditions. The observed HCHO and SO2 vertical column densities were significantly lower on fog days compared to clear days, reflecting a decreased chemical production of HCHO and aqueous phase oxidation of SO2 in fog droplets.

Vertical distributions of tropospheric SO2 based on MAX-DOAS observations: Investigating the impacts of regional transport at different heights in the boundary layer

Information on the vertical distribution of air pollutants is essential for understanding their spatiotemporal evolution underlying urban atmospheric environment. This paper presents the SO₂ profiles based on ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements from March 2018 to February 2019 in Hefei, East China. SO₂ decrease rapidly with increasing heights in the warm season, while lifted layers were observed in the cold season, indicating accumulation or long-range transport of SO₂ in different seasons might occur at different heights. The diurnal variations of SO₂ were roughly consistent for all four seasons, exhibiting the minimum at noon and higher values in the morning and late afternoon. Lifted layers of SO₂ were observed in the morning for fall and winter, implying the accumulation or transport of SO₂ in the morning mainly occurred at the top of the boundary layer. The bivariate polar plots showed that weighted SO₂ concentrations in the lower altitude were weakly dependent on wind, but in the middle and upper altitudes, higher weighted SO₂ concentrations were observed under conditions of middle-high wind speed. Concentration weighted trajectory (CWT) analysis suggested that potential sources of SO₂ in spring and summer were local and transported mainly occurred in the lower altitude from southern and eastern areas; while in fall and winter, SO₂ concentrations were deeply affected by long-range transport from northwestern and northern polluted regions in the middle and upper altitudes. Our findings provide new insight into the impacts of regional transport at different heights in the boundary layer on SO₂ pollution.

Reduction of Ionic Silver by Sulfur Dioxide as a Source of Silver Nanoparticles in the Environment

The natural formation of silver nanoparticles (AgNPs) via biotic and abiotic pathways in water and soil media contributes to the biogeochemical cycle of silver metal in the environment. However, the formation of AgNPs in the atmosphere has not been reported. Here, we describe a previously unreported source of AgNPs via the reduction of Ag(I) by SO₂ in the atmosphere, especially in moist environments, using multipronged advanced analytical and surface techniques. The rapid reduction of Ag(I) in the atmospheric aqueous phase was mainly caused by the sulfite ions formed from the dissolution of SO₂ in water, which contributed to the formation of AgNPs and was consistent with the Finke-Watzky model with a major contribution of the reduction-nucleation process. Sunlight irradiation excited SO₂ to form triplet SO₂, which reacted with water to form H₂SO₃ and greatly enhanced Ag(I) reduction and AgNP formation. Different pH values affected the speciation of Ag(I) and S(IV), which were jointly involved in the reduction of Ag(I). The formation of AgNPs was also observed in the atmospheric gas phase via direct reduction of Ag(I) by SO₂(gas), which occurred even in 50 ppbv SO₂(gas). The natural occurrence of AgNPs in the atmosphere may also be involved in silver corrosion, AgNP transformation and regeneration, detoxification of gaseous pollutants, and the sulfur cycle in the environment.

The Impact of Aerosols on Satellite Radiance Data Assimilation Using NCEP Global Data Assimilation System

Aerosol radiative effects have been studied extensively by climate and weather research communities. However, aerosol impacts on radiance in the context of data assimilation (DA) have received little research attention. In this study, we investigated the aerosol impacts on the assimilation of satellite radiances by incorporating time-varying three-dimensional aerosol distributions into the radiance observation operator. A series of DA experiments was conducted for August 2017. We assessed the aerosol impacts on the simulated brightness temperatures (BTs), bias correction and quality control (QC) algorithms for the assimilated infrared sensors, and analyzed temperature fields. We found that taking the aerosols into account reduces simulated BT in thermal window channels (8 to 13 μm) by up to 4 K over dust-dominant regions. The cooler simulated BTs result in more positive first-guess departures, produce more negative biases, and alter the QC checks about 20%/40% of total/assimilated observations at the wavelength of 10.39 μm. As a result, assimilating aerosol-affected BTs produces a warmer analyzed lower atmosphere and sea surface temperature which have better agreement with measurements over the trans-Atlantic region.

Application of WRF-Chem to simulate air quality over Northern Vietnam

The WRF-Chem (Weather Research and Forecasting with Chemistry) model is implemented and validated against ground-based observations for meteorological and atmospheric variables for the first time in Northern Vietnam. The WRF-Chem model was based on HTAPv2 emission inventory with MOZCART chemical-aerosol mechanism to simulate atmospheric variables for winter (January) and summer (July) of 2014. The model satisfactorily reproduces meteorological fields, such as temperature 2 m above the ground and relative humidity 2 m above the ground at 45 NCHMF meteorological stations in January, but lower agreement was found in those simulations of July. PM₁₀ and PM_2.5 concentrations in January showed good temporal and spatial agreements to observations recorded at three CEM air monitoring stations in Phutho, Quangninh, and Hanoi, with correlation coefficients of 0.36 and 0.59. However, WRF-Chem model was underestimated with MFBs from - 27.9 to - 118.7% for PM₁₀ levels and from - 34.2 to - 115.1% for PM_2.5 levels. It has difficulty in capturing day-by-day variation of PM₁₀ and PM_2.5 concentrations at each station in July, but MFBs were in the range from - 27.1 to - 40.2% which is slightly lower than those in January. It suggested that further improvements of the model and local emission data are needed to reduce uncertainties in modeling the distribution of atmospheric pollutants. Assessment of biomass burning emission on air quality in summer was analyzed to highlight the application aspect of the WRF-Chem model. The study may serve as a reference for future air quality modeling using WRF-Chem in Vietnam.

Description and Evaluation of the Fine Particulate Matter Forecasts in the NCAR Regional Air Quality Forecasting System

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications.

Assessing Sea-State Effects on Sea-Salt Aerosol Modeling in the Lower Atmosphere Using Lidar and In-Situ Measurements

Atmospheric-chemical coupled models usually parameterize sea-salt aerosol (SSA) emissions using whitecap fraction estimated considering only wind speed and ignoring sea state. This approach may introduce inaccuracies in SSA simulation. This study aims to assess the impact of sea state on SSA modeling, applying a new parameterization for whitecap fraction estimation based on wave age, calculated by the ratio between wave phase velocity and wind speed. To this end, the new parameterization was incorporated in the coupled Chemical Hydrological Atmospheric Ocean wave modeling System (CHAOS). CHAOS encompasses the wave model (WAM) two-way coupled through the OASIS3-MCT coupler with the Advanced Weather Research and Forecasting model coupled with Chemistry (WRF-ARW-Chem) and, thus, enabling the concurrent simulation of SSAs, wind speed and wave phase velocity. The simulation results were evaluated against in-situ and lidar measurements at 2 stations in Greece (Finokalia on 4 and 15 July 2014 and Antikythera-PANGEA on 15 September 2018). The results reveal significant differences between the parameterizations with the new one offering a more realistic representation of SSA levels in some layers of the lower atmosphere. This is attributed to the enhancement of the bubble-bursting mechanism representation with air-sea processes controlling whitecap fraction. Our findings also highlight the contribution of fresh wind-generated waves to SSA modeling.

Impact of the COVID-19 Pandemic Lockdown on Air Pollution in 20 Major Cities around the World

In order to fight against the spread of COVID-19, the most hard-hit countries in the spring of 2020 implemented different lockdown strategies. To assess the impact of the COVID-19 pandemic lockdown on air quality worldwide, Air Quality Index (AQI) data was used to estimate the change in air quality in 20 major cities on six continents. Our results show significant declines of AQI in NO2, SO2, CO, PM2.5 and PM10 in most cities, mainly due to the reduction of transportation, industry and commercial activities during lockdown. This work shows the reduction of primary pollutants, especially NO2, is mainly due to lockdown policies. However, preexisting local environmental policy regulations also contributed to declining NO2, SO2 and PM2.5 emissions, especially in Asian countries. In addition, higher rainfall during the lockdown period could cause decline of PM2.5, especially in Johannesburg. By contrast, the changes of AQI in ground-level O3 were not significant in most of cities, as meteorological variability and ratio of VOC/NOx are key factors in ground-level O3 formation.

Neighbourhood-scale dispersion of traffic-induced ultrafine particles in central London: WRF large eddy simulations

Traffic-generated ultrafine particles (UFPs) in the urban atmosphere have a high proportion of their composition comprised of semi-volatile compounds (SVOCs). The evaporation/condensation processes of these SVOCs can alter UFP number size distributions and play an important role in determining the fate of UFPs in urban areas. The neighbourhood-scale dispersion (over distances < 1 km) and evolution of traffic-generated UFPs for a real-world street network in central London was simulated by using the WRF-LES model (the large eddy simulation mode of the Weather Research and Forecasting modelling system) coupled with multicomponent microphysics. The neighbourhood scale dispersion of UFPs was significantly influenced by the spatial pattern of the real-world street emissions. Model output indicated the shrinkage of the peak diameter from the emitted profile to the downwind profile, due to an evaporation process during neighbourhood-scale dispersion. The dilution process and the aerosol microphysics interact with each other during the neighbourhood dispersion of UFPs, yielding model output that compares well with measurements made at a location downwind of an intense roadside source. The model captured the total SVOC concentrations well, with overestimations for gas concentrations and underestimations for particle concentrations, particularly of the lighter SVOCs. The contribution of the intense source, Marylebone Road (MR) in London, to concentrations at the downwind location (as estimated by a model scenario with emissions from MR only) is comparable with that of the rest of the street network (a scenario without emissions from MR), implying that both are important. An appreciable level of non-linearity is demonstrated for nucleation mode UFPs and medium range carbon SVOCs at the downwind receptor site.

Impact of dimethylsulfide chemistry on air quality over the Northern Hemisphere

We implement oceanic dimethylsulfide (DMS) emissions and its atmospheric chemical reactions into the Community Multiscale Air Quality (CMAQv53) model and perform annual simulations without and with DMS chemistry to quantify its impact on tropospheric composition and air quality over the Northern Hemisphere. DMS chemistry enhances both sulfur dioxide (SO2) and sulfate (S O 4 2 - ) over seawater and coastal areas. It enhances annual mean surface SO2 concentration by +46 pptv andS O 4 2 - by +0.33 μg/m3 and decreases aerosol nitrate concentration by -0.07 μg/m3 over seawater compared to the simulation without DMS chemistry. The changes decrease with altitude and are limited to the lower atmosphere. Impacts of DMS chemistry onS O 4 2 - are largest in the summer and lowest in the fall due to the seasonality of DMS emissions, atmospheric photochemistry and resultant oxidant levels. Hydroxyl and nitrate radical-initiated pathways oxidize 75% of the DMS while halogen-initiated pathways oxidize 25%. DMS chemistry leads to more acidic particles over seawater by decreasing aerosol pH. IncreasedS O 4 2 - from DMS enhances atmospheric extinction while lower aerosol nitrate reduces the extinction so that the net effect of DMS chemistry on visibility tends to remain unchanged over most of the seawater.

Assessing Desert Dust Indirect Effects on Cloud Microphysics through a Cloud Nucleation Scheme: A Case Study over the Western Mediterranean

In this study, the performance and characteristics of the advanced cloud nucleation scheme of Fountoukis and Nenes, embedded in the fully coupled Weather Research and Forecasting/Chemistry (WRF/Chem) model, are investigated. Furthermore, the impact of dust particles on the distribution of the cloud condensation nuclei (CCN) and the way they modify the pattern of the precipitation are also examined. For the simulation of dust particle concentration, the Georgia Tech Goddard Global Ozone Chemistry Aerosol Radiation and Transport of Air Force Weather Agency (GOCART-AFWA) is used as it includes components for the representation of dust emission and transport. The aerosol activation parameterization scheme of Fountoukis and Nenes has been implemented in the six-class WRF double-moment (WDM6) microphysics scheme, which treats the CCN distribution as a prognostic variable, but does not take into account the concentration of dust aerosols. Additionally, the presence of dust particles that may facilitate the activation of CCN into cloud or rain droplets has also been incorporated in the cumulus scheme of Grell and Freitas. The embedded scheme is assessed through a case study of significant dust advection over the Western Mediterranean, characterized by severe rainfall. Inclusion of CCN based on prognostic dust particles leads to the suppression of precipitation over hazy areas. On the contrary, precipitation is enhanced over areas away from the dust event. The new prognostic CCN distribution improves in general the forecasting skill of the model as bias scores, the root mean square error (RMSE), false alarm ratio (FAR) and frequencies of missed forecasts (FOM) are limited when modelled data are compared against satellite, LIDAR and aircraft observations.

Top-down vehicle emission inventory for spatial distribution and dispersion modeling of particulate matter

Emission inventories are one of the most critical inputs for the successful modeling of air quality. The performance of the modeling results is directly affected by the quality of atmospheric emission inventories. Consequently, the development of representative inventories is always required. Due to the lack of regional inventories in Brazil, this study aimed to investigate the use of the particulate matter (PM) emission estimation from the Brazilian top-down vehicle emission inventory (VEI) of 2012 for air quality modeling. Here, we focus on road vehicles since they are usually responsible for significant emissions of PM in urban areas. The total Brazilian emission of PM (63,000 t year^-1) from vehicular sources was distributed into the urban areas of 5557 municipalities, with 1-km² grid spacing, considering two approaches: (i) population and (ii) fleet of each city. A comparison with some local inventories is discussed. The inventory was compiled in the PREP-CHEM-SRC processor tool. One-month modeling (August 2015) was performed with WRF-Chem for the four metropolitan areas of Brazilian Southeast: Belo Horizonte (MABH), Great Vitória (MAGV), Rio de Janeiro (MARJ), and São Paulo (MASP). In addition, modeling with the Emission Database for Global Atmospheric Research (EDGAR) inventory was carried out to compare the results. Overall, EDGAR inventory obtained higher PM emissions than the VEI segregated by population and fleet, which is expected owing to considerations of additional sources of emission (e.g., industrial and residential). This higher emission of EDGAR resulted in higher PM₁₀ and PM_2.5 concentrations, overestimating the observations in MASP, while the proposed inventory well represented the ambient concentrations, obtaining better statistics indices. For the other three metropolitan areas, both EDGAR and the VEI inventories obtained consistent results. Therefore, the present work endorses the fact that vehicles are responsible for the more substantial contribution to PM emissions in the studied urban areas. Furthermore, the use of VEI can be representative for modeling air quality in the future.

Quantifying the impact of particle matter on mortality and hospitalizations in four Brazilian metropolitan areas

Air quality management involves investigating areas where pollutant concentrations are above guideline or standard values to minimize its effect on human health. Particulate matter (PM) is one of the most studied pollutants, and its relationship with health has been widely outlined. To guide the construction and improvement of air quality policies, the impact of PM on the four Brazilian southeast metropolitan areas was investigated. One-year long modeling of PM₁₀ and PM_2.5 was performed with the WRF-Chem model for 2015 to quantify daily and annual PM concentrations in 102 cities. Avoidable mortality due to diverse causes and morbidity due to respiratory and circular system diseases were estimated concerning WHO guidelines, which was adopted in Brazil as a final standard to be reached in the future; although there is no deadline set for its implementation yet. Results showed satisfactory representation of meteorology and ambient PM concentrations. An overestimation in PM concentrations for some monitoring stations was observed, mainly in São Paulo metropolitan area. Cities around capitals with high modelled annual PM_2.5 concentrations do not monitor this pollutant. The total avoidable deaths estimated for the region, related to PM_2.5, were 32,000 ± 5,300 due to all-cause mortality, between 16,000 ± 2,100 and 51,000 ± 3,000 due non-accidental causes, between 7,300 ± 1,300 and 16,700 ± 1,500 due to cardiovascular disease, between 4,750 ± 900 and 10,950 ± 870 due ischemic heart diseases and 1,220 ± 330 avoidable deaths due to lung cancer. Avoidable respiratory hospitalizations were greater for PM_2.5 among 'children' age group than for PM₁₀ (all age group) except in São Paulo metropolitan area. For circulatory system diseases, 9,840 ± 3,950 avoidable hospitalizations in the elderly related to a decrease in PM_2.5 concentrations were estimated. This study endorses that more restrictive air quality standards, human exposure, and health effects are essential factors to consider in urban air quality management.

Possible Impacts of Snow Darkening Effects on the Hydrological Cycle over Western Eurasia and East Asia

In this paper, we investigated the possible impact of snow darkening effect (SDE) by light-absorbing aerosols on the regional changes of the hydrological cycle over Eurasia using the NASA GEOS-5 Model with aerosol tracers and a state-of-the-art snow darkening module, the Goddard SnoW Impurity Module (GOSWIM) for the land surface. Two sets of ten-member ensemble experiments for 10 years were carried out forced by prescribed sea surface temperature (2002–2011) with different atmospheric initial conditions, with and without SDE, respectively. Results show that SDE can exert a significant regional influence in partitioning the contributions of evaporative and advective processes on the hydrological cycle, during spring and summer season. Over western Eurasia, SDE-induced rainfall increase during early spring can be largely explained by the increased evaporation from snowmelt. Rainfall, however, decreases in early summer due to the reduced evaporation as well as moisture divergence and atmospheric subsidence associated with the development of an anomalous mid- to upper-tropospheric anticyclonic circulation. On the other hand, in the East Asian monsoon region, moisture advection from the adjacent ocean is a main contributor to rainfall increase in the melting season. A warmer land-surface caused by earlier snowmelt and subsequent drying further increases moisture transport and convergence significantly enhancing rainfall over the region. Our findings suggest that the SDE may play an important role in leading to hotter and drier summers over western Eurasia, through coupled land-atmosphere interaction, while enhancing East Asian summer monsoonal precipitation via enhanced land-ocean thermal contrast and moisture transport due to the SDE-induced warmer Eurasian continent.

Hybrid Mass Balance/4D-Var Joint Inversion of NO x and SO2 Emissions in East Asia

Accurate estimates of NO x and SO2 emissions are important for air quality modeling and management. To incorporate chemical interactions of the two species in emission estimates, we develop a joint hybrid inversion framework to estimate their emissions in China and India (2005-2012). Pseudo observation tests and posterior evaluation with surface measurements demonstrate that joint assimilation of SO2 and NO2 can provide more accurate constraints on emissions than single-species inversions. This occurs through synergistic change of O3 and OH concentrations, particularly in conditions where satellite retrievals of the species being optimized have large uncertainties. The percentage changes of joint posterior emissions from the single-species posterior emissions go up to 242% at grid scales, although the national average of monthly emissions, seasonality, and interannual variations are similar. In China and India, the annual budget of joint posterior SO2 emissions is lower, but joint NO x posterior emissions are higher, because NO x emissions increase to increase SO2 concentration and better match Ozone Monitoring Instrument SO2 observations in high-NO x regions. Joint SO2 posterior emissions decrease by 16.5% from 2008 to 2012, while NO x posterior emissions increase by 24.9% from 2005 to 2011 in China-trends which are consistent with the MEIC inventory. Joint NO x and SO2 posterior emissions in India increase by 15.9% and 19.2% from 2005 to 2012, smaller than the 59.9% and 76.2% growth rate using anthropogenic emissions from EDGARv4.3.2. This work shows the benefit and limitation of joint assimilation in emission estimates and provides an efficient framework to perform the inversion.

SO2 Emission Estimates Using OMI SO2 Retrievals for 2005-2017

SO2 column densities from Ozone Monitoring Instrument provide important information on emission trends and missing sources, but there are discrepancies between different retrieval products. We employ three Ozone Monitoring Instrument SO2 retrieval products (National Aeronautics and Space Administration (NASA) standard (SP), NASA prototype, and BIRA) to study the magnitude and trend of SO2 emissions. SO2 column densities from these retrievals are most consistent when viewing angles and solar zenith angles are small, suggesting more robust emission estimates in summer and at low latitudes. We then apply a hybrid 4D-Var/mass balance emission inversion to derive monthly SO2 emissions from the NASA SP and BIRA products. Compared to HTAPv2 emissions in 2010, both posterior emission estimates are lower in United States, India, and Southeast China, but show different changes of emissions in North China Plain. The discrepancies between monthly NASA and BIRA posterior emissions in 2010 are less than or equal to 17% in China and 34% in India. SO2 emissions increase from 2005 to 2016 by 35% (NASA)-48% (BIRA) in India, but decrease in China by 23% (NASA)-33% (BIRA) since 2008. Compared to in situ measurements, the posterior GEOS-Chem surface SO2 concentrations have reduced NMB in China, the United States, and India but not in South Korea in 2010. BIRA posteriors have better consistency with the annual growth rate of surface SO2 measurement in China and spatial variability of SO2 concentration in China, South Korea, and India, whereas NASA SP posteriors have better seasonality. These evaluations demonstrate the capability to recover SO2 emissions using Ozone Monitoring Instrument observations.

Aerosol Indirect Effects on the Predicted Precipitation in a Global Weather Forecasting Model

Aerosol indirect effects on precipitation were investigated in this study using a Global/Regional Integrated Model system (GRIMs) linked with a chemistry package devised for reducing the heavy computational burden occurring in common atmosphere–chemistry coupling models. The chemistry package was based on the Goddard Chemistry Aerosol Radiation and Transport scheme of Weather Research and Forecasting with Chemistry (WRF-Chem), and five tracers that are relatively important for cloud condensation nuclei (CCN) formation were treated as prognostic variables. For coupling with the cloud physics processes in the GRIMs, the CCN number concentrations derived from the simplified chemistry package were utilized in the cumulus parameterization scheme (CPS) and the microphysics scheme (MPS). The simulated CCN number concentrations were higher than those used in original cloud physics schemes and, overall, the amount of incoming shortwave radiation reaching the ground was indirectly reduced by an increase in clouds owing to a high CCN. The amount of heavier precipitation increased over the tropics owing to the inclusion of enhanced riming effects under deep precipitating convection. The trend regarding the changes in non-convective precipitation was mixed depending on the atmospheric conditions. The increase in small-size cloud water owing to a suppressed autoconversion led to a reduction in precipitation. More precipitation can occur when ice particles fall under high CCN conditions owing to the accretion of cloud water by snow and graupel, along with their melting.

Assessing aerosol indirect effect on clouds and regional climate of East/South Asia and West Africa using NCEP GFS

Aerosols can act as cloud condensation nuclei and ice nuclei, resulting in changes in cloud droplet/particle number/size, and hence altering the radiation budget. This study investigates the interactions between aerosols and ice clouds by incorporating the latest ice clouds parameterization in an atmospheric general circulation model. The simulation shows a decrease in effective ice cloud crystal size corresponding to aerosol increase, referred to as the aerosol first indirect effect, which has not been comprehensively studied. Ice clouds with smaller particles reflect more shortwave radiation and absorb more infrared radiation, resulting in radiation change by 0.5-1.0 W/m2 at the top of the atmosphere (TOA). The TOA radiation field is also influenced by cloud cover change due to aerosol-induced circulation change. Such aerosol effects on precipitation highly depend on the existence of a deep convection system: interactions between aerosols and ice clouds create dipole precipitation anomalies in the Asian monsoon regions; while in West Africa, enhanced convections are constrained by anticyclone effects at high levels and little precipitation increase is found. We also conduct an experiment to assess interactions between aerosols and liquid clouds and compare the climatic effects with that due to ice clouds. Radiation and temperature changes generated by liquid clouds are normally 1-2 times larger than those generated by ice clouds. The radiation change has a closer relationship to liquid cloud droplet size than liquid cloud cover, in contrast with what we find for ice clouds.

Simulating Performance of CHIMERE on a Late Autumnal Dust Storm over Northern China

The accurate forecasting of dust emission and transport is a societal demand worldwide as dust pollution is part of many health, economic, and environment issues, which significantly impact sustainable development. The dust forecasting ability of present air quality forecast systems is mainly focused on spring dust events in East Asia, but further improvement may be needed as there is still difficulty in forecasting autumn dust activities, such as failing to predict the serious dust storm that occurred on 25 to 26 November 2018. In this study, a state-of-the-art air quality model, CHIMERE, with three coupled dust schemes was introduced for the first time to simulate the dust emissions during this event to qualitatively and quantitatively validate its dust simulating performance over Northern China. The model results reported that two of the three dust schemes were able to capture the dust emission source located in Gansu Province and reproduce the easterly dust transport path, showing moderately close agreement in the horizontal and vertical distribution patterns with the ground-based and satellite observations. The simulated PM10 concentration had a better relationship with the observed values with a correlation coefficient up to 0.96, while it was lower in the transported areas. Meanwhile, the simulations also presented incorrect dust emission positions such as in areas between the Hulun Buir sandy land and Horqin sandy land. Our results indicate that CHIMERE exhibits reasonably good performance regarding its dust simulation and forecast ability over this area, and its application would help to improve the dust analysis and forecast abilities in Northern China.

Spatial variation of modelled total, dry and wet nitrogen deposition to forests at global scale

Forests are an important biome that covers about one third of the global land surface and provides important ecosystem services. Since atmospheric deposition of nitrogen (N) can have both beneficial and deleterious effects, it is important to quantify the amount of N deposition to forest ecosystems. Measurements of N deposition to the numerous forest biomes across the globe are scarce, so chemical transport models are often used to provide estimates of atmospheric N inputs to these ecosystems. We provide an overview of approaches used to calculate N deposition in commonly used chemical transport models. The Task Force on Hemispheric Transport of Air Pollution (HTAP2) study intercompared N deposition values from a number of global chemical transport models. Using a multi-model mean calculated from the HTAP2 deposition values, we map N deposition to global forests to examine spatial variations in total, dry and wet deposition. Highest total N deposition occurs in eastern and southern China, Japan, Eastern U.S. and Europe while the highest dry deposition occurs in tropical forests. The European Monitoring and Evaluation Program (EMEP) model predicts grid-average deposition, but also produces deposition by land use type allowing us to compare deposition specifically to forests with the grid-average value. We found that, for this study, differences between the grid-average and forest specific could be as much as a factor of two and up to more than a factor of five in extreme cases. This suggests that consideration should be given to using forest-specific deposition for input to ecosystem assessments such as critical loads determinations.

Current and Future Disease Burden From Ambient Ozone Exposure in India

Long-term ambient ozone (O₃) exposure is a risk factor for human health. We estimate the source-specific disease burden associated with long-term O₃ exposure in India at high spatial resolution using updated risk functions from the American Cancer Society Cancer Prevention Study II. We estimate 374,000 (95UI: 140,000-554,000) annual premature mortalities using the updated risk function in India in 2015, 200% larger than estimates using the earlier American Cancer Society Cancer Prevention Study II risk function. We find that land transport emissions dominate the source contribution to this disease burden (35%), followed by emissions from power generation (23%). With no change in emissions by 2050, we estimate 1,126,000 (95UI: 421,000-1,667,000) annual premature mortalities, an increase of 200% relative to 2015 due to population aging and growth increasing the number of people susceptible to air pollution. We find that the International Energy Agency New Policy Scenario provides small changes (+1%) to this increasing disease burden from the demographic transition. Under the International Energy Agency Clean Air Scenario we estimate 791,000 (95UI: 202,000-1,336,000) annual premature mortalities in 2050, avoiding 335,000 annual premature mortalities (45% of the increase) compared to the scenario of no emission change. Our study highlights that critical public health benefits are possible with stringent emission reductions, despite population growth and aging increasing the attributable disease burden from O₃ exposure even under such strong emission reductions. The disease burden attributable to ambient fine particulate matter exposure dominates that from ambient O₃ exposure in the present day, while in the future, they may be similar in magnitude.

Impact of snow-darkening by deposition of light-absorbing aerosols on snow cover in the Himalaya-Tibetan-Plateau and influence on the Asian Summer monsoon: A possible mechanism for the Blanford Hypothesis

The impact of snow darkening by deposition of light absorbing aerosols (LAAs) on snow cover over the Himalaya-Tibetan-Plateau (HTP) and influence on the Asian summer monsoon are investigated using the NASA Goddard Earth Observing System Model Version 5 (GEOS-5). We find that during April-May-June, deposition of LAAs on snow leads to a reduction in surface albedo, initiating a sequence of feedback processes, starting with increased net surface solar radiation, rapid snowmelt in HTP and warming of the surface and upper troposphere, followed by enhanced low-level southwesterlies and increased dust loading over the Himalayas-Indo-Gangetic Plain. The warming is amplified by increased dust aerosol heating, and subsequently amplified by latent heating from enhanced precipitation over the Himalaya foothills and northern India, via the Elevated Heat Pump (EHP) effect during June-July-August. The reduced snow cover in the HTP anchors the enhanced heating over the Tibetan Plateau and its southern slopes, in conjunction with an enhancement of the Tibetan Anticyclone, and the development of an anomalous Rossby wavetrain over East Asia, leading to weakening of the subtropical westerly jet, and northward displacement and intensification of the Mei-Yu rainbelt. Our results suggest that atmosphere-land heating induced by LAAs, particularly desert dust play a fundamental role in physical processes underpinning the snow-monsoon relationship proposed by Blanford more than a century ago.

April 2008 Saharan dust event: Its contribution to PM10 concentrations over the Anatolian Peninsula and relation with synoptic conditions

An online-coupled regional Weather Research and Forecasting model with chemistry (WRF-Chem) is utilized incorporating 0.1°×0.1° spatial resolution HTAP (Hemispheric Transport of Air Pollution) anthropogenic emissions to investigate the spatial and temporal distribution of a Saharan dust outbreak, which contributed to high levels (>50μg/m³) of daily PM₁₀ concentrations over Turkey in April 2008. Aerosol optical depth and cloud optical thickness retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board of Aqua satellite are used to better analyze the synoptic conditions that generated the dust outbreak in April 2008. A "Sharav" low pressure system, which transports the dust from Saharan source region over Turkey along the cold front, tends to move faster in WRF-Chem simulations than observed. This causes the predicted dust event to arrive earlier than observed leading to an overestimation of surface PM₁₀ concentrations in WRF-Chem simulation at the beginning of the event.

Influence of Wave State and Sea Spray on the Roughness Length: Feedback on Medicanes

Occasionally, storms that share many features with tropical cyclones, including the presence of a quasi-circular “eye” a warm core and strong winds, are observed in the Mediterranean. Generally, they are known as Medicanes, or tropical-like cyclones (TLC). Due to the intense wind forcings and the consequent development of high wind waves, a large number of sea spray droplets—both from bubble bursting and spume tearing processes—are likely to be produced at the sea surface. In order to take into account this process, we implemented an additional Sea Spray Source Function (SSSF) in WRF-Chem, model version 3.6.1, using the GOCART (Goddard Chemistry Aerosol Radiation and Transport) aerosol sectional module. Traditionally, air-sea momentum fluxes are computed through the classical Charnock relation that does not consider the wave-state and sea spray effects on the sea surface roughness explicitly. In order to take into account these forcing, we implemented a more recent parameterization of the sea surface aerodynamic roughness within the WRF surface layer model, which may be applicable to both moderate and high wind conditions. The implemented SSSF and sea surface roughness parameterization have been tested using an operative model sequence based on COAWST (Coupled Ocean Atmosphere Wave Sediment Transport) and WRF-Chem. The third-generation wave model SWAN (Simulating Waves Nearshore), two-way coupled with the WRF atmospheric model in the COAWST framework, provided wave field parameters. Numerical simulations have been integrated with the WRF-Chem chemistry package, with the aim of calculating the sea spray generated by the waves and to include its effect in the Charnock roughness parametrization together with the sea state effect. A single case study is performed, considering the Medicane that affected south-eastern Italy on 26 September 2006. Since this Medicane is one of the most deeply analysed in literature, its investigation can easily shed some light on the feedbacks between sea spray and drag coefficients.

Stringent Emission Control Policies Can Provide Large Improvements in Air Quality and Public Health in India

Exposure to high concentrations of ambient fine particulate matter (PM2.5) is a leading risk factor for public health in India causing a large burden of disease. Business-as-usual economic and industrial growth in India is predicted to increase emissions, worsen air quality, and increase the associated disease burden in future decades. Here we use a high-resolution online-coupled model to estimate the impacts of different air pollution control pathways on ambient PM2.5 concentrations and human health in India. We find that with no change in emissions, the disease burden from exposure to ambient PM2.5 in 2050 will increase by 75% relative to 2015, due to population aging and growth increasing the number of people susceptible to air pollution. We estimate that the International Energy Agencies New Policy Scenario (NPS) and Clean Air Scenario (CAS) in 2050 can reduce ambient PM2.5 concentrations below 2015 levels by 9% and 68%, respectively, offsetting 61,000 and 610,000 premature mortalities a year, which is 9% and 91% of the projected increase in premature mortalities due to population growth and aging. Throughout India, the CAS stands out as the most effective scenario to reduce ambient PM2.5 concentrations and the associated disease burden, reducing the 2050 mortality rate per 100,000 below 2015 control levels by 15%. However, even under such stringent emission control policies, population growth and aging results in premature mortality estimates from exposure to particulate air pollution to increase by 7% compared to 2015, highlighting the challenge facing efforts to improve public health in India.

Extreme air pollution events in Hokkaido, Japan, traced back to early snowmelt and large-scale wildfires over East Eurasia: Case studies

To identify the unusual climate conditions and their connections to air pollutions in a remote area due to wildfires, we examine three anomalous large-scale wildfires in May 2003, April 2008, and July 2014 over East Eurasia, as well as how products of those wildfires reached an urban city, Sapporo, in the northern part of Japan (Hokkaido), significantly affecting the air quality. NASA's MERRA-2 (the Modern-Era Retrospective analysis for Research and Applications, Version 2) aerosol re-analysis data closely reproduced the PM2.5 variations in Sapporo for the case of smoke arrival in July 2014. Results show that all three cases featured unusually early snowmelt in East Eurasia, accompanied by warmer and drier surface conditions in the months leading to the fires, inducing long-lasting soil dryness and producing climate and environmental conditions conducive to active wildfires. Due to prevailing anomalous synoptic-scale atmospheric motions, smoke from those fires eventually reached a remote area, Hokkaido, and worsened the air quality in Sapporo. In future studies, continuous monitoring of the timing of Eurasian snowmelt and the air quality from the source regions to remote regions, coupled with the analysis of atmospheric and surface conditions, may be essential in more accurately predicting the effects of wildfires on air quality.

Origin, Maintenance and Variability of the Asian Tropopause Aerosol Layer (ATAL): The Roles of Monsoon Dynamics

Using NASA MERRA2 daily data, we investigated the origin, maintenance and variability of the Asian Tropopause Aerosol Layer (ATAL) in relation to variations of the Asia Monsoon Anticyclone (AMA) during the summer of 2008. During May-June, abundant quantities of carbon monoxide (CO), carbonaceous aerosols (CA) and dusts are found in the mid- and upper troposphere over India and China, arising from enhanced biomass burning emissions, as well as westerly transport from the Middle East deserts. During July-August, large quantities of dusts transported from the deserts are trapped and accumulate over the southern and eastern foothills of the Tibetan Plateau. Despite strong precipitation washout, ambient CO, CA and dust are lofted by orographically forced deep convection to great elevations, 12-16 km above sea level, via two key pathways over heavily polluted regions: a) the Himalayas-Gangetic Plain, and b) the Sichuan Basin. Upon entering the upper-troposphere-lower-stratosphere, the pollutants are capped by a stable layer near the tropopause, advected and dispersed by the anticyclonic circulation of AMA, forming the ATAL resembling a planetary-scale "double-stem chimney cloud". The development and variability of the ATAL are strongly linked to the seasonal march and intraseasonal (20-30 days and higher frequency) oscillations of the Asian monsoon.

Residential energy use emissions dominate health impacts from exposure to ambient particulate matter in India

Exposure to ambient fine particulate matter (PM_2.5) is a leading contributor to diseases in India. Previous studies analysing emission source attributions were restricted by coarse model resolution and limited PM_2.5 observations. We use a regional model informed by new observations to make the first high-resolution study of the sector-specific disease burden from ambient PM_2.5 exposure in India. Observed annual mean PM_2.5 concentrations exceed 100 μg m⁻³ and are well simulated by the model. We calculate that the emissions from residential energy use dominate (52%) population-weighted annual mean PM_2.5 concentrations, and are attributed to 511,000 (95UI: 340,000–697,000) premature mortalities annually. However, removing residential energy use emissions would avert only 256,000 (95UI: 162,000–340,000), due to the non-linear exposure–response relationship causing health effects to saturate at high PM_2.5 concentrations. Consequently, large reductions in emissions will be required to reduce the health burden from ambient PM_2.5 exposure in India.

Exposure to ambient particulate matter is a key contributor to disease in India and source attribution is vital for pollution control. Here the authors use a high-resolution regional model to show residential emissions dominate particulate matter concentrations and associated premature mortality.

Spatial and temporal estimates of population exposure to wildfire smoke during the Washington state 2012 wildfire season using blended model, satellite, and in situ data

In the western U.S., smoke from wild and prescribed fires can severely degrade air quality. Due to changes in climate and land management, wildfires have increased in frequency and severity, and this trend is expected to continue. Consequently, wildfires are expected to become an increasingly important source of air pollutants in the western U.S. Hence, there is a need to develop a quantitative understanding of wildfire-smoke-specific health effects. A necessary step in this process is to determine who was exposed to wildfire smoke, the concentration of the smoke during exposure, and the duration of the exposure. Three different tools have been used in past studies to assess exposure to wildfire smoke: in situ measurements, satellite-based observations, and chemical-transport model (CTM) simulations. Each of these exposure-estimation tools has associated strengths and weakness. We investigate the utility of blending these tools together to produce estimates of PM2.5 exposure from wildfire smoke during the Washington 2012 fire season. For blending, we use a ridge-regression model and a geographically weighted ridge-regression model. We evaluate the performance of the three individual exposure-estimate techniques and the two blended techniques by using leave-one-out cross validation. We find that predictions based on in situ monitors are more accurate for this particular fire season than the CTM simulations and satellite-based observations because of the large number of monitors present; therefore, blending provides only marginal improvements above the in situ observations. However, we show that in hypothetical cases with fewer surface monitors, the two blending techniques can produce substantial improvement over any of the individual tools.

West African monsoon decadal variability and surface-related forcings: Second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II)

The second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II) is designed to improve understanding of the possible roles and feedbacks of sea surface temperature (SST), land use land cover change (LULCC), and aerosols forcings in the Sahel climate system at seasonal to decadal scales. The project's strategy is to apply prescribed observationally based anomaly forcing, i.e., "idealized but realistic" forcing, in simulations by climate models. The goal is to assess these forcings' effects in producing/amplifying seasonal and decadal climate variability in the Sahel between the 1950s and the 1980s, which is selected to characterize the great drought period of the last century. This is the first multi-model experiment specifically designed to simultaneously evaluate such relative contributions. The WAMME II models have consistently demonstrated that SST forcing is a major contributor to the 20^th century Sahel drought. Under the influence of the maximum possible SST forcing, the ensemble mean of WAMME II models can produce up to 60% of the precipitation difference during the period. The present paper also addresses the role of SSTs in triggering and maintaining the Sahel drought. In this regard, the consensus of WAMME II models is that both Indian and Pacific Ocean SSTs greatly contributed to the drought, with the former producing an anomalous displacement of the Intertropical Convergence Zone (ITCZ) before the WAM onset, and the latter mainly contributes to the summer WAM drought. The WAMME II models also show that the impact of LULCC forcing on the Sahel climate system is weaker than that of SST forcing, but still of first order magnitude. According to the results, under LULCC forcing the ensemble mean of WAMME II models can produces about 40% of the precipitation difference between the 1980s and the 1950s. The role of land surface processes in responding to and amplifying the drought is also identified. The results suggest that catastrophic consequences are likely to occur in the regional Sahel climate when SST anomalies in individual ocean basins and in land conditions combine synergistically to favor drought.

Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia

Vegetation and peatland fires cause poor air quality and thousands of premature deaths across densely populated regions in Equatorial Asia. Strong El-Niño and positive Indian Ocean Dipole conditions are associated with an increase in the frequency and intensity of wildfires in Indonesia and Borneo, enhancing population exposure to hazardous concentrations of smoke and air pollutants. Here we investigate the impact on air quality and population exposure of wildfires in Equatorial Asia during Fall 2015, which were the largest over the past two decades. We performed high-resolution simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emission product. The model captures the spatio-temporal variability of extreme pollution episodes relative to space- and ground-based observations and allows for identification of pollution sources and transport over Equatorial Asia. We calculate that high particulate matter concentrations from fires during Fall 2015 were responsible for persistent exposure of 69 million people to unhealthy air quality conditions. Short-term exposure to this pollution may have caused 11,880 (6,153-17,270) excess mortalities. Results from this research provide decision-relevant information to policy makers regarding the impact of land use changes and human driven deforestation on fire frequency and population exposure to degraded air quality.

Spatiotemporal variability and contribution of different aerosol types to the Aerosol Optical Depth over the Eastern Mediterranean

This study characterizes the spatiotemporal variability and relative contribution of different types of aerosols to the Aerosol Optical Depth (AOD) over the Eastern Mediterranean as derived from MODIS Terra (3/2000-12/2012) and Aqua (7/2002-12/2012) satellite instruments. For this purpose, a 0.1° × 0.1° gridded MODIS dataset was compiled and validated against sunphotometric observations from the AErosol RObotic NETwork (AERONET). The high spatial resolution and long temporal coverage of the dataset allows for the determination of local hot spots like megacities, medium sized cities, industrial zones, and power plant complexes, seasonal variabilities, and decadal averages. The average AOD at 550 nm (AOD₅₅₀) for the entire region is ~ 0.22 ± 0.19 with maximum values in summer and seasonal variabilities that can be attributed to precipitation, photochemical production of secondary organic aerosols, transport of pollution and smoke from biomass burning in Central and Eastern Europe, and transport of dust from the Sahara Desert and the Middle East. The MODIS data were analyzed together with data from other satellite sensors, reanalysis projects and a chemistry-aerosol-transport model using an optimized algorithm tailored for the region and capable of estimating the contribution of different aerosol types to the total AOD₅₅₀. The spatial and temporal variability of anthropogenic, dust and fine mode natural aerosols over land and anthropogenic, dust and marine aerosols over the sea is examined. The relative contribution of the different aerosol types to the total AOD₅₅₀ exhibits a low/high seasonal variability over land/sea areas, respectively. Overall, anthropogenic aerosols, dust and fine mode natural aerosols account for ~ 51 %, ~ 34 % and ~ 15 % of the total AOD₅₅₀ over land, while, anthropogenic aerosols, dust and marine aerosols account ~ 40 %, ~ 34 % and ~ 26 % of the total AOD₅₅₀ over the sea, based on MODIS Terra and Aqua observations.

Spatiotemporal variability and contribution of different aerosol types to the Aerosol Optical Depth over the Eastern Mediterranean

This study characterizes the spatiotemporal variability and relative contribution of different types of aerosols to the Aerosol Optical Depth (AOD) over the Eastern Mediterranean as derived from MODIS Terra (3/2000-12/2012) and Aqua (7/2002-12/2012) satellite instruments. For this purpose, a 0.1° × 0.1° gridded MODIS dataset was compiled and validated against sunphotometric observations from the AErosol RObotic NETwork (AERONET). The high spatial resolution and long temporal coverage of the dataset allows for the determination of local hot spots like megacities, medium sized cities, industrial zones, and power plant complexes, seasonal variabilities, and decadal averages. The average AOD at 550 nm (AOD₅₅₀) for the entire region is ~ 0.22 ± 0.19 with maximum values in summer and seasonal variabilities that can be attributed to precipitation, photochemical production of secondary organic aerosols, transport of pollution and smoke from biomass burning in Central and Eastern Europe, and transport of dust from the Sahara Desert and the Middle East. The MODIS data were analyzed together with data from other satellite sensors, reanalysis projects and a chemistry-aerosol-transport model using an optimized algorithm tailored for the region and capable of estimating the contribution of different aerosol types to the total AOD₅₅₀. The spatial and temporal variability of anthropogenic, dust and fine mode natural aerosols over land and anthropogenic, dust and marine aerosols over the sea is examined. The relative contribution of the different aerosol types to the total AOD₅₅₀ exhibits a low/high seasonal variability over land/sea areas, respectively. Overall, anthropogenic aerosols, dust and fine mode natural aerosols account for ~ 51 %, ~ 34 % and ~ 15 % of the total AOD₅₅₀ over land, while, anthropogenic aerosols, dust and marine aerosols account ~ 40 %, ~ 34 % and ~ 26 % of the total AOD₅₅₀ over the sea, based on MODIS Terra and Aqua observations.

The Regional Impacts of Cooking and Heating Emissions on Ambient Air Quality and Disease Burden in China

Exposure to air pollution is a major risk factor globally and particularly in Asia. A large portion of air pollutants result from residential combustion of solid biomass and coal fuel for cooking and heating. This study presents a regional modeling sensitivity analysis to estimate the impact of residential emissions from cooking and heating activities on the burden of disease at a provincial level in China. Model surface PM2.5 fields are shown to compare well when evaluated against surface air quality measurements. Scenarios run without residential sector and residential heating emissions are used in conjunction with the Global Burden of Disease 2013 framework to calculate the proportion of deaths and disability adjusted life years attributable to PM2.5 exposure from residential emissions. Overall, we estimate that 341 000 (306 000-370 000; 95% confidence interval) premature deaths in China are attributable to residential combustion emissions, approximately a third of the deaths attributable to all ambient PM2.5 pollution, with 159 000 (142 000-172 000) and 182 000 (163 000-197 000) premature deaths from heating and cooking emissions, respectively. Our findings emphasize the need to mitigate emissions from both residential heating and cooking sources to reduce the health impacts of ambient air pollution in China.