National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD

High spatial resolution estimating of exposure to particulate matter 2.5 (PM2.5) is currently very limited in China. This study uses the newly released nationwide, hourly PM2.5 concentrations to create a nationwide, geographically weighted regression (GWR) model to estimate ground-level PM2.5 concentrations in China. A3 km resolution aerosol optical depth (AOD) product from MODIS is used as the primary predictor. Fire emissions detected by MODIS fire count were considered in the model development process. Additionally, meteorological features were used as covariates in the model to improve the estimation of ground-level PM2.5 concentrations. The model performed well and explained 81% of the daily PM2.5 concentration variations in model predictions, and the cross validations R2 is 0.79. The cross-validated root mean squared error (RMSE) of the model was 18.6 μg/m3.Annual PM2.5 concentrations retrieved by the MODIS 3 km AOD product indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. Estimated high-resolution national-scale daily PM2.5 maps are useful to identify severe air pollution episodes and determine health risk assessments. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions, especially for regions without PM monitoring sites

Study of PBLH and Its Correlation with Particulate Matter from One-Year Observation over Nanjing, Southeast China

The Planetary Boundary Layer Height (PBLH) plays an important role in the formation and development of air pollution events. Particulate Matter is one of major pollutants in China. Here, we present the characteristics of PBLH through three-methods of Lidar data inversion and show the correlation between the PBLH and the PM2.5 (PM2.5 with the diameter 75 μg/m3 and the PM2.5 \u3c 35 μg/m3 in daytime, respectively. The low PBLH often occurs with condition of the low wind speed and high relative humidity, which will lead to high PM2.5 concentration and the low visibility. On the other hand, the stability of PBL is enhanced by high PM concentration and low visibility

Satellite-based PM2.5 Exposure Estimation and Health Impacts over China

Exposure to suspended fine particulate matter (PM2.5) has been proven to adversely impact public health through increased risk of cardiovascular and respiratory mortality. Assessing health impacts of PM2.5 and its long-term variations requires accurate estimates of large-scale exposure data. Such data include mass concentration and particle size, the latter of which may be an effect modifier on PM2.5 attributable health risks. The availability of these exposure data, however, is limited by sparse ground-level monitoring networks. In this dissertation, an optical-mass relationship was first developed based on aerosol microphysical characteristics for ground-level PM2.5 retrieval. This method quantifies PM2.5 mass concentrations with a theoretical basis, which can simultaneously estimate large-scale particle size. The results demonstrate the effectiveness and applicability of the proposed method and reveal the spatiotemporal distribution of PM2.5 over China. To explore the spatial variability and population exposure, particle radii of PM2.5 are then derived using the developed theoretical relationship along with a statistical model for a better performance. The findings reveal the prevalence of exposure to small particles (i.e. PM1), identify the need for in-situ measurements of particle size, and motivate further research to investigate the effects of particle size on health outcomes. Finally, the long-term impacts of PM2.5 on health and environmental inequality are assessed by using the satellite-retrieved PM2.5 estimates over China during 2005-2017. Premature mortality attributable to PM2.5 exposure increased by 31% from 2005 to 2017. For some causes of death, the burden fell disproportionately on provinces with low-to-middle GDP per capita. As a whole, this work contributes to bridging satellite remote sensing and long-term exposure studies and sheds light on an ongoing need to understand the effects of PM2.5, including both concentrations and other particle characteristics, on human health

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