SDG11: Sustainable Cities and Communities
Monitoring and analyzing fine particulate matter (PM2.5) in China（2019）
Scale: National
Study area: China
Fine particulate matter (PM2.5) is a primary air pollutant in China that is responsible for negatively impacting the health of local populations. Since 2012, several national environmental protection departments have paid close attention to the spread of PM2.5. Additional ground-measurement stations are constructed on an annual basis for monitoring the pollutant. However, these stations have an uneven distribution and spatial discontinuity. The historical data on this pollutant is also lacking and is difficult to obtain; therefore, it is difficult to conduct any study on the epidemiological and health effects of fine particles.
Satellite remote sensing has the advantages of long-term time series data and broad spatial coverage, which can compensate for the lack of site observations. Remote sensing imagery has been widely used by scientists to estimate the concentration of PM2.5.
Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including assessing air quality and municipal and other waste management.
Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g., PM2.5 and PM/ in cities (population weighted).
Method
Many methods have improved the estimation of PM2.5 using Aerosol Optical Depth (AOD). These methods employ different characteristics to obtain historical PM2.5 concentrations, which have applications in the evaluation of public health risks. The objective of this study was to analyze the changes of PM2.5 in key cities in China in recent years. This was accomplished by calculating the average annual concentration of PM2.5 in the built-up areas of key cities from 2010 to 2018 according to the population weight. Calculations were obtained using the following equation:
Cagg=SUM(Cnat*Pnat)/SUM(Pnat)
where Cagg is the estimation at the global scale, Cnat is the estimation at the country scale, and Pnat is the national population.
Data used in this case
Remote sensing data and related products included MODIS AOD and MODIS NDVI in the time series. Monitoring data included atmospheric composition of China's environmental monitoring stations, meteorological data, and reanalysis by ECMWF.
Results and analysis
The MODIS AOD products from the National Aeronautics and Space Administration (NASA) Terra and Aqua satellites were used to estimate the PM2.5 concentration from 2010 to 2018 in China (Figure 1). In general, the spatial pattern of the annual PM2.5 over China showed high correlation with the accumulation of both population and industries. Elevated PM2.5 levels were mostly concentrated on those cities or city-clusters with higher urbanization or industrialization in central and eastern China. Temporally, the annual nationally averaged PM2.5 presented an overall decreasing trend from 2013 to 2018. This clearly demonstrates the effectiveness of comprehensive pollution control measures conducted by the Chinese government in recent years.
Figure 1. Annual average distribution of PM2.5 in China from 2010 to 2018.
| Highlights |
| Average annual PM25 data products have been developed for the period from 2010 to 2018. The Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu-Chongqing regions showed an overall decreasing trend from 2010 to 2018. |
Outlook
Deep learning methods will continue to be explored in the future. More relevant indicators and parameters will be introduced to improve estimation accuracy. The mechanism and source distribution of pollutants in the atmosphere will also be explored to promote atmospheric research.
In terms of application and promotion, the atmospheric environment affects human health, which is the primary concern of the public. The study will improve and promote the progress and application of PM2.5, Ozone and other products closely related to public health through the construction of high spatial and temporal resolution data. Meanwhile, it also needs the guidance and support from the government, society and other users.