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

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

Working with the enemy? Social work education and men who use intimate partner violence

This article examines service user involvement in social work education. It discusses the challenges and ethical considerations of involving populations who may previously have been excluded from user involvement initiatives, raising questions about the benefits and challenges of their involvement. The article then provides discussion of an approach to service user involvement in social work education with one of these populations, men who use violence in their intimate relationships, and concludes by considering the implications of their involvement for the social work academy

Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns

The Yonsei Aerosol Retrieval (YAER) algorithm for the Geostationary Ocean Color Imager (GOCI) retrieves aerosol optical properties only over dark surfaces, so it is important to mask pixels with bright surfaces. The Advanced Himawari Imager (AHI) is equipped with three shortwave-infrared and nine infrared channels, which is advantageous for bright-pixel masking. In addition, multiple visible and near-infrared channels provide a great advantage in aerosol property retrieval from the AHI and GOCI. By applying the YAER algorithm to 10 min AHI or 1 h GOCI data at 6km x 6km resolution, diurnal variations and aerosol transport can be observed, which has not previously been possible from low-Earth-orbit satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), as well as bias correction based on normalized difference vegetation indexes, and (2) compilation of the fused product using ensemble-mean and maximum-likelihood estimation (MLE) methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error (RMSE), and median bias error than the retrieved result for each product. If the RMSE and mean AOD bias values used for MLE fusion are correct, the MLE fused products show better accuracy, but the ensemble-mean products can still be useful as MLE

Exploring Himawari-8 geostationary observations for the advanced coastal monitoring of the Great Barrier Reef

Larissa developed an algorithm to enable water-quality assessment within the Great Barrier Reef (GBR) using weather satellite observations collected every 10 minutes. This unprecedented temporal resolution records the dynamic nature of water quality fluctuations for the entire GBR, with applications for improved monitoring and management

Monitoring Aerosol Optical Depth for Air Quality Through Himawari-8 in Urban Area West Java Province Indonesia

Air quality is a crucial parameter in human life. One air quality indicator can be observed through Aerosol Optical Depth (AOD). If these substances are pollutants such as particulate matter, aerosols, and ozone, it is confident that air quality will deteriorate, threatening human health and causing climate change. AOD monitoring can be used as a basis for policymakers and related parties to maintain the stability of air quality in the atmosphere. Many ground observation stations monitor air quality by obtaining data on PM2.5 and PM10 aerosol particles. However, the number of ground stations is limited, resulting in incomplete data. Fortunately, remote sensing satellites have the advantage of covering large areas and providing continuous observations, with the ability to gather information on large-scale aerosol and obtain spatiotemporal distribution. Therefore, this research aims to obtain AOD through Himawari-8 and analyze the spatiotemporal air quality in urban areas of West Java based on AOD. The research methodology used in this study is descriptive analysis with an empirical research approach. Assisted by remote sensing technology and Geographic Information Systems, this research generates AOD data extraction that can be obtained from the new generation satellite of Himawari-8. The distribution of AOD levels and spatiotemporal monitoring in urban areas of West Java is very dynamic depending on anthropogenic activity in a particular area and time.   Keywords: Aerosol Optical Depth (AOD), Air Quality, Himawari-

A Machine Learning Algorithm for Himawari-8 Total Suspended Solids Retrievals in the Great Barrier Reef

Remote sensing of ocean colour has been fundamental to the synoptic-scale monitoring of marine water quality in the Great Barrier Reef (GBR). However, ocean colour sensors onboard low orbit satellites, such as the Sentinel-3 constellation, have insufficient revisit capability to fully resolve diurnal variability in highly dynamic coastal environments. To overcome this limitation, this work presents a physics-based coastal ocean colour algorithm for the Advanced Himawari Imager onboard the Himawari-8 geostationary satellite. Despite being designed for meteorological applications, Himawari-8 offers the opportunity to estimate ocean colour features every 10 min, in four broad visible and near-infrared spectral bands, and at 1 km2 spatial resolution. Coupled ocean–atmosphere radiative transfer simulations of the Himawari-8 bands were carried out for a realistic range of in-water and atmospheric optical properties of the GBR and for a wide range of solar and observation geometries. The simulated data were used to develop an inverse model based on artificial neural network techniques to estimate total suspended solids (TSS) concentrations directly from the Himawari-8 top-of-atmosphere spectral reflectance observations. The algorithm was validated with concurrent in situ data across the coastal GBR and its detection limits were assessed. TSS retrievals presented relative errors up to 75% and absolute errors of 2 mg L−1 within the validation range of 0.14 to 24 mg L−1, with a detection limit of 0.25 mg L−1. We discuss potential applications of Himawari-8 diurnal TSS products for improved monitoring and management of water quality in the GBR

Simultaneous assimilation of Fengyun-4A and Himawari-8 aerosol optical depth retrieval to improve air quality simulations during one storm event over East Asia

Aerosols are the main components of air pollutants, which are closely related to haze, dust storm and air pollution. In this study, an aerosol data assimilation system was developed using Gridpoint Statistical Interpolation (GSI) system to assimilate the Aerosol Optical Depth (AOD) observations from FY4 and Himawari-8 for the first time and applied in the heavy dust case over east Asia in March 2018. Three parallel experiments assimilated AOD from FY4, Himawari-8 and both the FY4 and Himawari-8 respectively and a control experiment which did not employ DA were performed. The hourly aerosol analyses and forecasts are compared with the assimilated FY-4 AOD, Himawari-8 AOD and independent AOD from Aerosol Robotic Network (AERONET). The results showed that all forms of DA experiments improved a low Bias and the RMSE reduced about 20%. The aerosol data assimilation with observations from both the FY-4 and Himawari-8 satellites substantially improved aerosol analyses and subsequent forecasts with more abundant aerosol observation information, especially over the northwest of China. This study indicates that the new generation geostationary meteorological satellites have potential to dramatically contribute to air quality forecasting

General Comparison of FY-4A/AGRI With Other GEO/LEO Instruments and Its Potential and Challenges in Non-meteorological Applications

Meteorological satellites have become an indispensable tool for weather and land observation. Traditionally, geostationary (GEO) satellites have been used in operational meteorological services due to their high temporal resolution, while polar-orbiting satellites, with their high spatial resolution, are applied more to monitor environmental change and natural disasters. The development of China’s next-generation geostationary meteorological satellites (the FY-4 series) represents an exciting expansion of Chinese non-meteorological remote sensing capabilities. The first satellite (FY-4A) of the FY-4 series was launched on 11 December 2016. The Advanced Geosynchronous Radiation Imager (AGRI) on board FY-4A has 14 spectral bands (increased from the 5 bands of FY-2) that are quantized with 12 bits per pixel (up from 10 bits for FY-2) and sampled at 1 km at nadir in the visible (VIS), 2 km in the near-infrared (NIR), and 4 km in the remaining IR spectral bands (compared with 1.25 km for VIS, no NIR, and 5 km for IR of FY-2). In later satellites in the FY-4A series, the AGRI channel number will be gradually increased from 14 to 18 with IR spatial resolution of 2 km, and the full-disk temporal resolution will be enhanced from 15 to 5 min. With their improved spectral, spatial, and temporal resolution properties, the FY-4 series will gradually approach low earth orbiting (LEO) sensors in spatial and spectral resolution, which will offer greater opportunity and capability for observing small objects and rapid changes in land, ocean, and atmosphere. This review paper provides an introduction to the Chinese FY-4 observation capabilities, a comparison of FY-4 with other new-generation GEO and LEO weather satellites, and associated non-meteorological applications. A series of typical examples based on recent and on-going operational work in National Satellite Meteorological Center of China Meteorological Administration (NSMC/CMA) that use FY-4A data for non-meteorological applications are demonstrated and discussed, including (i) aerosol monitoring, (ii) dust monitoring, (iii) volcanic ash detection and aviation applications, (iv) fire detection and dynamical evaluation, (v) water body detection, and (vi) floating algae monitoring. The paper concludes with a synthesis of these application areas and the challenges that CMA has to address for future research, technological innovation, and in-depth applications

Towards a comprehensive view of dust events from multiple satellite and ground measurements: exemplified by the May 2017 East Asian dust storm

One or several aspects of the source, distribution, transport, and optical properties of airborne dust have been characterized using different types of satellite and ground measurements, each with unique advantages. In this study, a dust event that occurred over the East Asia area in May 2017 was exemplified to demonstrate how all the above-mentioned aspects of a dust event can be pictured by combining the advantages of different satellite and ground measurements. The data used included the Himawari-8 satellite Advanced Himawari Imager (AHI) true-colour images, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol vertical profiles, the Aura satellite Ozone Monitoring Instrument (OMI) aerosol index images, and the ground-based Aerosol Robotic Network (AERONET) aerosol properties and the ground station particulate matter (PM) measurements. From the multi-satellite/sensor (AHI, CALIOP, and OMI) time series observations, the dust storm was found to originate from the Gobi Desert on the morning of 3 May 2017 and transport north-eastward to the Bering Sea, eastward to the Korean Peninsula and Japan, and southward to south-central China. The air quality in China deteriorated drastically: the PM10 (PM&thinsp;&lt;&thinsp;10&thinsp;µm in aerodynamic diameter) concentrations measured at some air quality stations located in northern China reached 4333&thinsp;µg&thinsp;m−3. At the AOE_Baotou, Beijing, Xuzhou-CUMT, and Ussuriysk AERONET sites, the maximum aerosol optical depth values reached 2.96, 2.13, 2.87, and 0.65 and the extinction Ångström exponent dropped to 0.023, 0.068, 0.03, and 0.097, respectively. The dust storm also induced unusual aerosol spectral single-scattering albedo and volume size distribution.</p