Long-range transport of CO and aerosols from Siberian biomass burning over northern Japan during 18-20 May 2016

High CO concentration and dense aerosol layers at 1-6 km altitude in the free troposphere were observed over Rikubetsu, Japan, in ground-based Fourier transform spectrometer (FTS) and lidar measurements during 18-20 May 2016, days after intense wildfires east of Lake Baikal, Siberia. The column-averaged dry-air mole fraction of CO (XCO) was observed to be ∼150 ppb from 11:15 to 13:50 JST on 19 May, and peak aerosol optical depths (AODs) of 1.41 and 1.28 were observed at 15:40 JST 18 May and 11:20 JST 19 May, respectively. We used the HYSPLIT model to calculate five-day backward trajectories from Rikubetsu on May 18, 2016 at 2, 3 and 5 km altitude. The results show that the air parcels passed over the Siberian wildfires during 16-17 May. It was found that the high CO concentrations originated from forest fires were transported to the upper layers of Hokkaido. This will contribute to the understanding of the regional effects of air pollution in northern Japan due to air masses originating from forest fires. By combining these independent datasets such as AERONET aerosol optical thickness (AOT), MODIS fire data, and Infrared Atmospheric Sounding Interferometer (IASI) total CO columns, we confirmed that the lidar measurements of enhanced aerosol concentrations and FTS measurements of maximum XCO over Rikubetsu resulted from a persistent smoke plume transported from Siberian wildfires. Relatively large-scale forest fires have been frequently occurring in Siberia recently. However, the effects of CO and other gases released from them over northern Japan are not well known. We observed high concentrations of CO over the TCCON station in Rikubetsu, Japan, which we believe to be of forest fire origin. Therefore, we analyzed it as a case study to confirm its origin and impact on the upper atmosphere over northern Japan.

Assessment of air quality in North Korea from satellite observations

North Korea's air quality is poorly understood due to a lack of reliable data. Here, we analyzed urban- to national-scale air quality changes in North Korea using multi-year satellite observations. Pyongyang, Nampo, Pukchang, and Munchon were identified as pollution hotspots. On a national scale, we found that North Korea experienced 6.7, 17.8, and 20.6 times greater amounts of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO) per unit primary energy supply (PES) than South Korea from 2005 to 2018. Besides, North Korea had a 24.3 times larger aerosol optical depth (AOD) per PES than South Korea from 2011 to 2018. Severe CO and aerosol pollution is aligned with extensive biofuel combustion. High SO₂ pollution corresponds with the strong coal dependence of the industry. The change rates of the national average columns for NO₂, SO₂, and CO were + 3.6, -4.4, and -0.4 % yr^-1, respectively. The AOD change rate was -4.8 % yr^-1. Overall decreasing trends, except for NO₂, are likely due to a decline in coal-fired PES. Positive NO₂ trends are consistent with increasing industrial activities. Each pollutant showed consistent patterns of linear trends, even after correcting the influence of transboundary pollution. Flue gas control and biofuel consumption reduction seem necessary to improve North Korea's air quality.

New seasonal pattern of pollution emerges from changing North American wildfires

Rising emissions from wildfires over recent decades in the Pacific Northwest are known to counteract the reductions in human-produced aerosol pollution over North America. Since amplified Pacific Northwest wildfires are predicted under accelerating climate change, it is essential to understand both local and transported contributions to air pollution in North America. Here, we find corresponding increases for carbon monoxide emitted from the Pacific Northwest wildfires and observe significant impacts on both local and down-wind air pollution. Between 2002 and 2018, the Pacific Northwest atmospheric carbon monoxide abundance increased in August, while other months showed decreasing carbon monoxide, so modifying the seasonal pattern. These seasonal pattern changes extend over large regions of North America, to the Central USA and Northeast North America regions, indicating that transported wildfire pollution could potentially impact the health of millions of people.

Growing emissions from Pacific Northwest wildfires have increased atmospheric carbon monoxide in August, raising questions about potential health impacts as the seasonal pattern of air quality changes for large regions of North America.

A Pre-Operational System Based on the Assimilation of MODIS Aerosol Optical Depth in the MOCAGE Chemical Transport Model

In this study we present a pre-operational forecasting assimilation system of different types of aerosols. This system has been developed within the chemistry-transport model of Météo-France, MOCAGE, and uses the assimilation of the Aerosol Optical Depth (AOD) from MODIS (Moderate Resolution Imaging Spectroradiometer) onboard both Terra and Aqua. It is based on the AOD assimilation system within the MOCAGE model. It operates on a daily basis with a global configuration of 1∘×1∘ (longitude × latitude). The motivation of such a development is the capability to predict and anticipate extreme events and their impacts on the air quality and the aviation safety in the case of a huge volcanic eruption. The validation of the pre-operational system outputs has been done in terms of AOD compared against the global AERONET observations within two complete years (January 2018–December 2019). The comparison between both datasets shows that the correlation between the MODIS assimilated outputs and AERONET over the whole period of study is 0.77, whereas the biases and the RMSE (Root Mean Square Error) are 0.006 and 0.135, respectively. The ability of the pre-operational system to predict extreme events in near real time such as the desert dust transport and the propagation of the biomass burning was tested and evaluated. We particularly presented and documented the desert dust outbreak which occurred over Greece in late March 2018 as well as the wildfire event which happened on Australia between July 2019 and February 2020. We only presented these two events, but globally the assimilation chain has shown that it is capable of predicting desert dust events and biomass burning aerosols which happen all over the globe.

Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ

Global coupled chemistry-climate models underestimate carbon monoxide (CO) in the Northern Hemisphere, exhibiting a pervasive negative bias against measurements peaking in late winter and early spring. While this bias has been commonly attributed to underestimation of direct anthropogenic and biomass burning emissions, chemical production and loss via OH reaction from emissions of anthropogenic and biogenic volatile organic compounds (VOCs) play an important role. Here we investigate the reasons for this underestimation using aircraft measurements taken in May and June 2016 from the Korea-United States Air Quality (KORUS-AQ) experiment in South Korea and the Air Chemistry Research in Asia (ARIAs) in the North China Plain (NCP). For reference, multispectral CO retrievals (V8J) from the Measurements of Pollution in the Troposphere (MOPITT) are jointly assimilated with meteorological observations using an ensemble adjustment Kalman filter (EAKF) within the global Community Atmosphere Model with Chemistry (CAM-Chem) and the Data Assimilation Research Testbed (DART). With regard to KORUS-AQ data, CO is underestimated by 42% in the control run and by 12% with the MOPITT assimilation run. The inversion suggests an underestimation of anthropogenic CO sources in many regions, by up to 80% for northern China, with large increments over the Liaoning Province and the North China Plain (NCP). Yet, an often-overlooked aspect of these inversions is that correcting the underestimation in anthropogenic CO emissions also improves the comparison with observational O₃ datasets and observationally constrained box model simulations of OH and HO₂. Running a CAM-Chem simulation with the updated emissions of anthropogenic CO reduces the bias by 29% for CO, 18% for ozone, 11% for HO₂, and 27% for OH. Longer-lived anthropogenic VOCs whose model errors are correlated with CO are also improved, while short-lived VOCs, including formaldehyde, are difficult to constrain solely by assimilating satellite retrievals of CO. During an anticyclonic episode, better simulation of O₃, with an average underestimation of 5.5 ppbv, and a reduction in the bias of surface formaldehyde and oxygenated VOCs can be achieved by separately increasing by a factor of 2 the modeled biogenic emissions for the plant functional types found in Korea. Results also suggest that controlling VOC and CO emissions, in addition to widespread NO _x controls, can improve ozone pollution over East Asia.

Variability in Observation-based Onroad Emission Constraints from a Near-road Environment

This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NOx) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NOx ratios (ΔCO:ΔNOx) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NOx ratios are used to infer NOx emission adjustments for a priori emissions estimates from EPA's MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NOx scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ΔCO:ΔNOx from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115m and 300m) increases OLS and orthogonal regression estimates of ΔCO:ΔNOx but not cross-road gradient ΔCO:ΔNOx. The inferred NOx emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NOx emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ΔCO:ΔNOx estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.

The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future

The Dark Target aerosol algorithm was developed to exploit the information content available from the observations of Moderate-Resolution Imaging Spectroradiometers (MODIS), to better characterize the global aerosol system. The algorithm is based on measurements of the light scattered by aerosols toward a space-borne sensor against the backdrop of relatively dark Earth scenes, thus giving rise to the name “Dark Target”. Development required nearly a decade of research that included application of MODIS airborne simulators to provide test beds for proto-algorithms and analysis of existing data to form realistic assumptions to constrain surface reflectance and aerosol optical properties. This research in itself played a significant role in expanding our understanding of aerosol properties, even before Terra MODIS launch. Contributing to that understanding were the observations and retrievals of the growing Aerosol Robotic Network (AERONET) of sun-sky radiometers, which has walked hand-in-hand with MODIS and the development of other aerosol algorithms, providing validation of the satellite-retrieved products after launch. The MODIS Dark Target products prompted advances in Earth science and applications across subdisciplines such as climate, transport of aerosols, air quality, and data assimilation systems. Then, as the Terra and Aqua MODIS sensors aged, the challenge was to monitor the effects of calibration drifts on the aerosol products and to differentiate physical trends in the aerosol system from artefacts introduced by instrument characterization. Our intention is to continue to adapt and apply the well-vetted Dark Target algorithms to new instruments, including both polar-orbiting and geosynchronous sensors. The goal is to produce an uninterrupted time series of an aerosol climate data record that begins at the dawn of the 21st century and continues indefinitely into the future.

Drivers of seasonal and annual air pollution exposure in a complex urban environment with multiple source contributions

Outdoor air pollution is a global health concern, but detailed exposure information is still limited for many parts of the world. In this study, high-resolution exposure surfaces were generated for annual and seasonal fine particulate matter (PM_2.5), coarse particulate matter (PM₁₀), and carbon monoxide (CO) for the Greater Beirut Area (GBA), Lebanon, an urban zone with a complex topography and multiple source contributions. Land use regression models (LUR) were calibrated and validated with monthly data collected from 58 locations between March 2017 and March 2018. The annual mean (±1 SD) concentrations of PM_2.5, PM₁₀, and CO across the monitoring locations were 68.1 (±15.7) μg/m³, 83.5 (±19.5) μg/m³, and 2.48 (±1.12) ppm, respectively. The coefficients of determination for LUR models ranged from 56 to 67% for PM_2.5, 44 to 63% for the PM₁₀ models, and 50 to 60% for the CO. LUR model structures varied significantly by season for both PM_2.5 and PM₁₀ but not for CO. Traffic emissions were consistently the main source of CO emissions throughout the year. The relative importance of industrial emissions and power generation sources towards predicted PM levels increased during the hot season while the contribution of the international airport diminished. Moreover, the complex topography of the study area along with the seasonal changes in the predominant wind directions affected the spatial predicted concentrations of all three pollutants. Overall, the predicted exposure surfaces were able to conserve the inter-pollution correlations determined from the field monitoring campaign, with the exception of the cold season. Our pollution surfaces suggest that the entire population of Beirut is regularly exposed to concentrations exceeding the World Health Organization (WHO) air quality standards for both PM_2.5 and PM₁₀.

Satellite measurements of aerosol optical depth and carbon monoxide and comparison with ground data

Satellite data of aerosol optical depths (AODs) from the moderate resolution imaging spectroradiometer (MODIS) and carbon monoxide (CO) columns from the measurements of pollution in the troposphere (MOPITT) were collected for the study in Northern Thailand. Comparative analyses were conducted of MODIS (Terra and Aqua) AODs with ground particulate matter with diameter below 10 microns (PM₁₀) concentrations and MOPITT CO surface/total columns with ground CO concentrations for 2014-2017. Temporal variations in both the satellite and ground datasets were in good agreement. High levels of air pollutants were common during March-April. The annual analysis of both satellite and ground datasets revealed the highest levels of air pollutants in 2016 and the lowest levels in 2017. The AODs and PM₁₀ concentrations were at higher levels in the morning than in the afternoon. The comparison between satellite products showed that AODs correlated better with the CO total columns than the CO surface columns. The regression analysis presented better performance of Aqua AODs-PM₁₀ than Terra AODs-PM₁₀ with correlation coefficients (r) of 0.72-0.83 and 0.57-0.79, respectively. Ground CO concentrations correlated better with MOPITT CO surface columns (r = 0.65-0.73) than with CO total columns (r = 0.56-0.72). The r values of satellite and ground datasets were greatest when the analysis was restricted to November-March (dry weather periods with possible low mixing height (MH)). Overall, the results suggested that the relationships between satellite and ground data can be used to develop predictive models for ground PM₁₀ and CO in northern Thailand, particularly during air pollution episodes located where ground monitoring stations are limited.

Satellites See the World’s Atmosphere

Satellite meteorology is a relatively new branch of the atmospheric sciences. The field emerged in the late 1950s during the Cold War and built on the advances in rocketry after World War II. In less than 70 years, satellite observations have transformed the way scientists observe and study Earth. This paper discusses some of the key advances in our understanding of the energy and water cycles, weather forecasting, and atmospheric composition enabled by satellite observations. While progress truly has been an international achievement, in accord with a monograph observing the centennial of the American Meteorological Society, as well as limited space, the emphasis of this chapter is on the U.S. satellite effort.

The role of satellite observations in understanding the impact of El Niño on the carbon cycle: current capabilities and future opportunities

The 2015/2016 El Niño was the first major climate variation when there were a range of satellite observations that simultaneously observed land, ocean and atmospheric properties associated with the carbon cycle. These data are beginning to provide new insights into the varied responses of land ecosystems to El Niño, but we are far from fully exploiting the information embodied by these data. Here, we briefly review the atmospheric and terrestrial satellite data that are available to study the carbon cycle. We also outline recommendations for future research, particularly the closer integration of satellite data with forest biometric datasets that provide detailed information about carbon dynamics on a range of timescales.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

Spatio-Temporal Characteristics of Tropospheric Ozone and Its Precursors in Guangxi, South China

The temporal and spatial distributions of tropospheric ozone and its precursors (NO2, CO, HCHO) are analyzed over Guangxi (GX) in South China. We used tropospheric column ozone (TCO) from the Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) onboard the Aura satellite (OMI/MLS), NO2 and HCHO from OMI and CO from the Measurements of Pollution in the Troposphere (MOPITT) instrument in the period 2005–2016. The TCO shows strong seasonality, with the highest value in spring and the lowest value observed in the monsoon season. The seasonal variation of HCHO is similar to that of TCO, while NO2 and CO show slightly different patterns with higher values in spring and winter compared to lower values in autumn and summer. The surface ozone, NO2 and CO observed by national air quality monitoring network sites are also compared with satellite-observed TCO, NO2 and CO, showing good agreement for NO2 and CO but a different seasonal pattern for ozone. Unlike TCO, surface ozone has the highest value in autumn and the lowest value in winter. To reveal the difference, the vertical profiles of ozone and CO from the measurement of ozone and water vapor by airbus in-service aircraft (MOZAIC) observations over South China are also examined. The seasonal averaged vertical profiles of ozone and CO show obvious enhancements at 2–6 km altitudes in spring. Furthermore, we investigate the dependence of TCO and surface ozone on meteorology and transport in detail along with the ECMWF reanalysis data, Tropical Rainfall Measuring Mission (TRMM) 3BV42 dataset, OMI ultraviolet index (UV index) dataset, MODIS Fire Radiative Power (FRP) and back trajectory. Our results show that the wind pattern at 800 hPa plays a significant role in determining the seasonality of TCO over GX, especially for the highest value in spring. Trajectory analysis, combined with MODIS FRP suggests that the air masses that passed through the biomass burning (BB) region of Southeast Asia (SEA) induced the enhancement of TCO and CO in the upper-middle troposphere in spring. However, the seasonal cycle of surface ozone is associated with wind patterns at 950 hPa, and the contribution of the photochemical effect is offset by the strong summer monsoon, which results in the maximum surface ozone concentration in post-monsoon September. The variations in the meteorological conditions at different levels and the influence of transport from SEA can account for the vertical distribution of ozone and CO. We conclude that the seasonal distribution of TCO results from the combined impact of meteorology and long-term transport.

On the feasibility of monitoring carbon monoxide in the lower troposphere from a constellation of Northern Hemisphere geostationary satellites: global scale assimilation experiments (Part II)

This paper describes the second phase of an Observing System Simulation Experiment (OSSE) that utilizes the synthetic measurements from a constellation of satellites measuring atmospheric composition from geostationary (GEO) Earth orbit presented in part I of the study. Our OSSE is focused on carbon monoxide observations over North America, East Asia and Europe where most of the anthropogenic sources are located. Here we assess the impact of a potential GEO constellation on constraining northern hemisphere (NH) carbon monoxide (CO) using data assimilation. We show how cloud cover affects the GEO constellation data density with the largest cloud cover (i.e., lowest data density) occurring during Asian summer. We compare the modeled state of the atmosphere (Control Run), before CO data assimilation, with the known "true" state of the atmosphere (Nature Run) and show that our setup provides realistic atmospheric CO fields and emission budgets. Overall, the Control Run underestimates CO concentrations in the northern hemisphere, especially in areas close to CO sources. Assimilation experiments show that constraining CO close to the main anthropogenic sources significantly reduces errors in NH CO compared to the Control Run. We assess the changes in error reduction when only single satellite instruments are available as compared to the full constellation. We find large differences in how measurements for each continental scale observation system affect the hemispherical improvement in long-range transport patterns, especially due to seasonal cloud cover. A GEO constellation will provide the most efficient constraint on NH CO during winter when CO lifetime is longer and increments from data assimilation associated with source regions are advected further around the globe.

Vertically-resolved profiles of mass concentrations and particle backscatter coefficients of Asian dust plumes derived from lidar observations of silicon dioxide

This study presents a method to retrieve vertically-resolved profiles of dust mass concentrations by analyzing Raman lidar signals of silicon dioxide (quartz) at 546nm. The observed particle plumes consisted of mixtures of East Asian dust with anthropogenic pollution. Our method for the first time allows for extracting the contribution of the aerosol component "pure dust" contained in the aerosol type "polluted dust". We also propose a method that uses OPAC (Optical Properties of Aerosols and Clouds) and the mass concentrations profiles of dust in order to derive profiles of backscatter coefficients of pure dust in mixed dust/pollution plumes. The mass concentration of silicon dioxide (quartz) in the atmosphere can be estimated from the backscatter coefficient of quartz. The mass concentration of dust is estimated by the weight percentage (38-77%) of mineral quartz in Asian dust. The retrieved dust mass concentrations are classified into water soluble, nucleation, accumulation, mineral-transported and coarse mode according to OPAC. The mass mixing ratio of 0.018, 0.033, 0.747, 0.130 and 0.072, respectively, is used. Dust extinction coefficients at 550nm were calculated by using OPAC and prescribed number concentrations for each of the 5 components. Dust backscatter coefficients were calculated from the dust extinction coefficients on the basis of a lidar ratio of 45±3sr at 532nm. We present results of quartz-Raman measurements carried out on the campus of the Gwangju Institute of Science and Technology (35.10°N, 126.53°E) on 15, 16, and 21 March 2010.

Approaches to Observe Anthropogenic Aerosol-Cloud Interactions

Anthropogenic aerosol particles exert an-quantitatively very uncertain-effective radiative forcing due to aerosol-cloud interactions via an immediate altering of cloud albedo on the one hand and via rapid adjustments by alteration of cloud processes and by changes in thermodynamic profiles on the other hand. Large variability in cloud cover and properties and the therefore low signal-to-noise ratio for aerosol-induced perturbations hamper the identification of effects in observations. Six approaches are discussed as a means to isolate the impact of anthropogenic aerosol on clouds from natural cloud variability to estimate or constrain the effective forcing. These are (i) intentional cloud modification, (ii) ship tracks, (iii) differences between the hemispheres, (iv) trace gases, (v) weekly cycles and (vi) trends. Ship track analysis is recommendable for detailed process understanding, and the analysis of weekly cycles and long-term trends is most promising to derive estimates or constraints on the effective radiative forcing.

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5.

Satellite monitoring for carbon monoxide and particulate matter during forest fire episodes in Northern Thailand

This study explored the use of satellite data to monitor carbon monoxide (CO) and particulate matter (PM) in Northern Thailand during the dry season when forest fires are known to be an important cause of air pollution. Satellite data, including Measurement of Pollution in the Troposphere (MOPITT) CO, Moderate Resolution Imaging Spectroradiometer aerosol optical depth (MODIS AOD), and MODIS fire hotspots, were analyzed with air pollution data measured at nine automatic air quality monitoring stations in the study area for February-April months of 2008-2010. The correlation analysis showed that daily CO and PM with size below 10 μm (PM10) were associated with the forest fire hotspot counts, especially in the rural areas with the maximum correlation coefficient (R) of 0.59 for CO and 0.65 for PM10. The correlations between MODIS AOD and PM10, between MOPITT CO and CO, and between MODIS AOD and MOPITT CO were also analyzed, confirming the association between these variables. Two forest fire episodes were selected, and the dispersion of pollution plumes was studied using the MOPITT CO total column and MODIS AOD data, together with the surface wind vectors. The results showed consistency between the plume dispersion, locations of dense hotspots, ground monitoring data, and prevalent winds. The satellite data were shown to be useful in monitoring the regional transport of forest fire plumes.

Intercontinental transport of aerosols and photochemical oxidants from Asia and its consequences

The intercontinental transport of aerosols and photochemical oxidants from Asia is a crucial issue for air quality concerns in countries downwind of the significant emissions and concentrations of pollutants occurring in this important region of the world. Since the lifetimes of some important pollutants are long enough to be transported over long distance in the troposphere, regional control strategies for air pollution in downwind countries might be ineffective without considering the effects of long-range transport of pollutants from Asia. Field campaigns provide strong evidence for the intercontinental transport of Asian pollutants. They, together with ground-based observations and model simulations, show that the air quality over parts of North America is being affected by the pollutants transported from Asia. This paper examines the current understanding of the intercontinental transport of gases and aerosols from Asia and resulting effects on air quality, and on the regional and global climate system.

Interactive effects of solar UV radiation and climate change on biogeochemical cycling

This report assesses research on the interactions of UV radiation (280-400 nm) and global climate change with global biogeochemical cycles at the Earth's surface. The effects of UV-B (280-315 nm), which are dependent on the stratospheric ozone layer, on biogeochemical cycles are often linked to concurrent exposure to UV-A radiation (315-400 nm), which is influenced by global climate change. These interactions involving UV radiation (the combination of UV-B and UV-A) are central to the prediction and evaluation of future Earth environmental conditions. There is increasing evidence that elevated UV-B radiation has significant effects on the terrestrial biosphere with implications for the cycling of carbon, nitrogen and other elements. The cycling of carbon and inorganic nutrients such as nitrogen can be affected by UV-B-mediated changes in communities of soil organisms, probably due to the effects of UV-B radiation on plant root exudation and/or the chemistry of dead plant material falling to the soil. In arid environments direct photodegradation can play a major role in the decay of plant litter, and UV-B radiation is responsible for a significant part of this photodegradation. UV-B radiation strongly influences aquatic carbon, nitrogen, sulfur and metals cycling that affect a wide range of life processes. UV-B radiation changes the biological availability of dissolved organic matter to microorganisms, and accelerates its transformation into dissolved inorganic carbon and nitrogen, including carbon dioxide and ammonium. The coloured part of dissolved organic matter (CDOM) controls the penetration of UV radiation into water bodies, but CDOM is also photodegraded by solar UV radiation. Changes in CDOM influence the penetration of UV radiation into water bodies with major consequences for aquatic biogeochemical processes. Changes in aquatic primary productivity and decomposition due to climate-related changes in circulation and nutrient supply occur concurrently with exposure to increased UV-B radiation, and have synergistic effects on the penetration of light into aquatic ecosystems. Future changes in climate will enhance stratification of lakes and the ocean, which will intensify photodegradation of CDOM by UV radiation. The resultant increase in the transparency of water bodies may increase UV-B effects on aquatic biogeochemistry in the surface layer. Changing solar UV radiation and climate also interact to influence exchanges of trace gases, such as halocarbons (e.g., methyl bromide) which influence ozone depletion, and sulfur gases (e.g., dimethylsulfide) that oxidize to produce sulfate aerosols that cool the marine atmosphere. UV radiation affects the biological availability of iron, copper and other trace metals in aquatic environments thus potentially affecting metal toxicity and the growth of phytoplankton and other microorganisms that are involved in carbon and nitrogen cycling. Future changes in ecosystem distribution due to alterations in the physical and chemical climate interact with ozone-modulated changes in UV-B radiation. These interactions between the effects of climate change and UV-B radiation on biogeochemical cycles in terrestrial and aquatic systems may partially offset the beneficial effects of an ozone recovery.