Air Quality over China

The strong economic growth in China in recent decades, together with meteorological factors, has resulted in serious air pollution problems, in particular over large industrialized areas with high population density. To reduce the concentrations of pollutants, air pollution control policies have been successfully implemented, resulting in the gradual decrease of air pollution in China during the last decade, as evidenced from both satellite and ground-based measurements. The aims of the Dragon 4 project “Air quality over China” were the determination of trends in the concentrations of aerosols and trace gases, quantification of emissions using a top-down approach and gain a better understanding of the sources, transport and underlying processes contributing to air pollution. This was achieved through (a) satellite observations of trace gases and aerosols to study the temporal and spatial variability of air pollutants; (b) derivation of trace gas emissions from satellite observations to study sources of air pollution and improve air quality modeling; and (c) study effects of haze on air quality. In these studies, the satellite observations are complemented with ground-based observations and modeling

The Applicability of Remote Sensing in the Field of Air Pollution

This report prepared by KNMI and JRC is the final result of a study on the applicability of remote sensing in the field of air pollution requested by the DG Environment. The objectives of this study were to: Have an assessment of presently available scientific information on the feasibility of utilising remote sensing techniques in the implementation of existing legislation, and describe opportunities for realistic streamlining of monitoring in air quality and emissions, based on greater use of remote sensing. Have recommendations for the next policy cycle on the use of remote sensing through development of appropriate provisions and new concepts, including, if appropriate, new environmental objectives, more suited to the use of remote sensing. Have guidance on how to effectively engage with GMES and other initiatives in the air policy field projects Satellite remote sensing of the troposphere is a rapidly developing field. Today several satellite sensors are in orbit that measure trace gases and aerosol properties relevant to air quality. Satellite remote sensing data have the following unique properties: Near-simultaneous view over a large area; Global coverage; Good spatial resolution. The properties of satellite data are highly complementary to ground-based in-situ networks, which provide detailed measurements at specific locations with a high temporal resolution. Although satellite data have distinct benefits, the interpretation is often less straightforward as compared to traditional in-situ measurements. Maps of air pollution measured from space are widespread in the scientific community as well as in the media, and have had a strong impact on the general public and the policy makers. The next step is to make use of satellite data in a quantitative way. Applications based solely on satellite data are foreseen, however an integrated approach using satellite data, ground-based data and models combined with data assimilation, will make the best use of the satellite remote-sensing potential, as well as of the synergy with ground-based observations.JRC.H.4-Transport and air qualit

Air Quality Research Using Remote Sensing

Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

Determining ground-level composition and concentration of particulate matter across regional areas using the Himawari-8 satellite

Speciated ground-level aerosol concentrations are required to understand and mitigate health impacts from dust storms, wildfires and other aerosol emissions. Globally, surface monitoring is limited due to cost and infrastructure demands. While remote sensing can help estimate respirable (i.e. ground level) concentrations, current observations are restricted by inadequate spatiotemporal resolution, uncertainty in aerosol type, particle size, and vertical profile. One key issue with current remote sensing datasets is that they are derived from reflectances observed by polar orbiting imagers, which means that aerosol is only derived during the daytime, and only once or twice per day. Sub-hourly, infrared (IR), geostationary data, such as the ten-minute data from Himawari-8, are required to monitor these events to ensure that sporadic dust events can be continually observed and quantified. Newer quantification methods using geostationary data have focussed on detecting the presence, or absence, of a dust event. However, limited attention has been paid to the determination of composition, and particle size, using IR wavelengths exclusively. More appropriate IR methods are required to quantify and classify aerosol composition in order to improve the understanding of source impacts. The primary research objectives were investigated through a series of scientific papers centred on aspects deemed critical to successfully determining ground-level concentrations. A literature review of surface particulate monitoring of dust events using geostationary satellite remote sensing was undertaken to understand the theory and limitations in the current methodology. The review identified (amongst other findings) the reliance on visible wavelengths and the lack of temporal resolution in polar-orbiting satellite data. As a result of this, a duststorm was investigated to determine how rapidly the storm passed and what temporal data resolution is required to monitor these and other similar events. Various IR dust indices were investigated to determine which are optimum for determining spectral change. These indices were then used to qualify and quantitate dust events, and the methodology was validated against three severe air quality events of a dust storm; smoke from prescribed burns; and an ozone smog incident. The study identified that continuous geostationary temporal resolution is critical in the determination of concentration. The Himawari-8 spatial resolution of 2 km is slightly coarse and further spatial aggregation or cloud masking would be detrimental to determining concentrations. Five dual-band BTD combinations, using all IR wavelengths, maximises the identification of compositional differences, atmospheric stability, and cloud cover and this improves the estimated accuracy. Preliminary validation suggests that atmospheric stability, cloud height, relative humidity, PM2.5, PM10, NO, NO2, and O3 appear to produce plausible plumes but that aerosol speciation (soil, sea-spray, fires, vehicles, and secondary sulfates) and SO2 require further investigation. The research described in the thesis details the processes adopted for the development and implementation of an integrated approach to using geostationary remote sensing data to quantify population exposure (who), qualify the concentration and composition (what), assess the temporal (when) and spatial (where) concentration distributions, to determine the source (why) of aerosols contribution to resulting ground-level concentration

Assessment of Biomass Burning and Mineral Dust Impacts on Air Quality and Regional Climate

East Asia is frequently influenced by dust storms and biomass burning. This study conducts a comprehensive investigation of its kind based on data analysis with surface measurements, satellite products, and model simulations. The objective of this study is to improve the understanding of the impacts of biomass burning and dust on air quality and regional climate. The study period covers March and April from 2006 to 2010. Biomass burning from Peninsular Southeast Asia (PSEA) has significant annual variations by up to 60% within the study period. The impact of biomass burning on air quality is mainly confined within the upper air due to the uplift motion driven by lee-side trough along eastern side of Tibet Plateau. The Weather Research and Forecasting and Community Multiscale Air Quality (WRF/CMAQ) system successfully reproduces the spatial distributions and temporal variations of air pollutants. Simulation bias falls in the range of 10%~50%, mainly due to the uncertainties within the emission inventory. This study reveals that the default WRF/CMAQ model has doubt counting of the soil moisture effect and subsequently underestimates dust emission by 55%. The microphysical parameterization and the speciation profile are revised to characterize the emission and mass contribution of dust better. Heterogeneous dust chemistry is also incorporated. These modifications substantially improve the model performance as indicated by the comparison between model simulations and observations. This study reveals that biomass burning has significant warming effect due to the presence of the underlying stratocumulus cloud. Biomass burning aerosol cools the near surface air by -0.2K, and significantly warms the upper air by up to +2K. Dust aerosol cools the near surface air by -0.9K and warms the upper air by +0.1K. This is the first investigation into the coexistence of biomass burning and dust over East Asia. This coexistence changes the aerosol direct radiative effect efficiencies of both biomass burning and dust by ±10%

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM

Spatial Information Technology Based Modeling Approach for Air Pollution Assessment

It is an accepted fact that our atmosphere bears an increasing load of pollutants: carbon dioxide, ozone, oxides of nitrogen and sulfur, volatile organic compounds (VOCs), particulates, and heavy metals. The adverse health and environment effects of air pollution have been a major concern in shaping our environmental quality. The World Health Organization (WHO) estimates that 1.5 billion people living in the urban areas throughout the world are exposed to dangerous levels of air pollution and 2 million premature deaths occur annually. A year shortening of life expectancy by an average is also the result of air pollution. Air pollution risk assessment, especially in urban areas, is currently one of the most important environmental issues for human health. Air quality model is a useful tool to simulate the complex dispersion of pollutants in the atmosphere and to predict the long-term effects on ground and spatial levels, and it plays an important role in air pollution risk assessment. Since there are inherent complexities and uncertainties associated with land use information, meteorological conditions, emission spatial allocation, as well as physical and chemical reactions in air quality modeling, it still needs to be further explored. The emergences of new spatial information technologies, such as satellite remote sensing technology and Geographic Information Systems (GIS) open a new era for air quality modeling and air pollution risk assessment, making it possible to predict the spatial concentration distributions of air pollutants on larger scales with finer details. The objectives of the work in this thesis include the development of GIS-based air quality modeling system to predict the spatial concentration distributions of ambient air pollutants (PM2.5, NO2, SO2, and CO), the development of satellite remote sensing approach to retrieve aerosol optical depth (AOD) and to derive ground-level pollutant concentrations (PM2.5 and NO2), and the development of fuzzy aggregation risk assessment approach to evaluate the health risks of multiple air pollutants. A GIS-based multi-source and multi-box (GMSMB) air quality modeling approach is developed to predict the spatial concentration distribution of four air pollutants (PM2.5, NO2, SO2, and CO) for the state of California. A satellite remote sensing approach is investigated to derive the ground-level NO2 concentrations from the satellite Ozone Monitoring Instrument (OMI) tropospheric NO2 column data for the same location and same period. The GMSMB modeling and satellite-derived results are cross-verified through comparing with each other and with the in-situ surface measurements. Furthermore, a fuzzy aggregation-ordered weighted averaging (OWA) risk assessment approach is developed to evaluate the integrated health risks of the four air pollutants. An improved aerosol optical depth (AOD) retrieval algorithm is proposed for the MODIS satellite instrument at 1-km resolution. In order to estimate surface reflectances over variable cover types, including bright and dark surfaces, a modified minimum reflectance technique (MRT) is used. A new lookup table (LUT) is created using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) Radiative Transfer Code for the presumed aerosol types. The MODIS-retrieved AODs are used to derive the ground-level PM2.5 concentrations using the aerosol vertical profiles obtained from the GEOS-Chem simulation. The developed method has been examined to retrieve the AODs and evaluate the concentration distribution of PM2.5 over the city of Montreal, Canada in 2009. The satellite-derived PM2.5 concentrations are ranging from 1 to 14 µg/m3 in Montreal, which are in good agreement with the in-situ surface measurements at all monitoring stations. This suggests that the method in this study can retrieve AODs at a higher spatial resolution than previously and can operate on an urban scale for PM2.5 assessment. Furthermore, the ground-level PM2.5 concentrations and corresponding health risks are investigated using the retrieved AOD from the satellite instruments of MODIS and MISR for the extended East Asia, including China, India, Japan, and South Korea. The results are validated with the monitoring values and literatures. Depending on the regression analysis, the GDP growth rates, population growth rates, and coal consumptions are the main reasons of the higher PM2.5 concentrations in Beijing. Some mitigating measurements are then proposed and the future trend is predicted. The developed method can be used to other regions for making cost-effective strategy to control and improve air pollution

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

GEMS will monitor air quality over Asia at unprecedented spatial and temporal resolution from GEO for the first time, providing column measurements of aerosol, ozone and their precursors (nitrogen dioxide, sulfur dioxide and formaldehyde). Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in late 2019 - early 2020 to monitor Air Quality (AQ) at an unprecedented spatial and temporal resolution from a Geostationary Earth Orbit (GEO) for the first time. With the development of UV-visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO and aerosols) can be obtained. To date, all the UV-visible satellite missions monitoring air quality have been in Low Earth orbit (LEO), allowing one to two observations per day. With UV-visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be onboard the GEO-KOMPSAT-2 satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager (GOCI)-2. These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA's TEMPO and ESA's Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS)

IMPACT OF AEROSOL DIRECT AND INDIRECT EFFECTS ON EAST ASIAN AIR QUALITY DURING EAST-AIRE CAMPAIGN PERIOD

WRF-Chem simulations were performed for the March 2005 East Asian Studies of Tropospheric Aerosols: an International Regional Experiment (EAST-AIRE) Intensive Observation Campaign (IOC) to investigate the effects of aerosols on surface radiation and air quality. Domain-wide, WRF-Chem showed a decrease of 20 W/m2 in surface shortwave (SW) radiation due to the aerosol direct effect (ADE), consistent with observational studies. The ADE reduced mixing and caused 24-hr surface PM2.5 concentrations to increase in eastern China (4.4%), southern China (10%), western China (2.3%), and the Sichuan Basin (9.6%), due to a thinner planetary boundary layer (PBL) and increased stability. Conversely, surface 1-hour maximum ozone was reduced by 2.3% domain-wide and up to 12% in eastern China because less radiation reached the surface. Studies of the impact of reducing SO2 and black carbon (BC) emissions by 80% on aerosol amounts were performed via two sensitivity simulations. Reducing SO2 decreased surface PM2.5 concentrations in the Sichuan Basin and southern China by 5.4% and decreased ozone by up to 6 ppbv in the Sichuan Basin and Southern China. Reducing BC emissions decreased PM2.5 by 3% in eastern China and the Sichuan Basin but increased surface ozone by up to 3.6 ppbv in eastern China and the Sichuan Basin. This result indicates that the benefits of reducing PM2.5 associated with reducing absorbing aerosols may be partially offset by increases in ozone at least for a scenario when NOx and VOC emissions are unchanged. The relative importance of direct and indirect effects in altering the atmospheric composition was then studied with two case studies of periods that featured strong synoptic systems. The case studies demonstrated that changes in primary aerosol (i.e., dust, OC) and chemically stable trace gas concentrations (i.e., CO) were mainly driven by changes in meteorological conditions due to the direct and indirect effects with the direct effect showing a stronger impact over highly polluted and dry regions and the indirect effect dominating over humid areas. Secondary aerosols (i.e., sulfate, nitrate) were affected by both changes in meteorological and chemical processes The variation of ozone due to the indirect effect was found to be associated with changes in the NO2 photolysis rate due to changes in actinic flux driven by changes in AOD and/or COD