Alok Mukherjee
Monitoring PM2.5 and Air Quality in Beijing, China
Abstract
Beijing, China is a highly populated city located in the northeast of mainland China,
reported at 21,510,000 residents as of 2013, whose air quality is currently poor due to high levels
of urban smog generated by neighboring industries. The primary motivation for the further study
of air quality in China comes mainly from the impact of this smog on human health, and concern
for the residents of Beijing who are regularly exposed to these air quality conditions. This
proposal plans to focus on the monitoring of smog-related particles, PM2.5, or particulate matter
measuring 2.5 microns in diameter or smaller, as a main contributor of urban smog in Beijing,
where, although various data sets exist in the form of single point, ground based sensing
monitors, a more comprehensive view of the geographic distribution of air quality in Beijing
could be determined through the application of remote sensing techniques like satellite
monitoring. This data would be desirable both in order to increase awareness of air quality
hazards on human health as well as to critique the ground based sensor data already existing.
Introduction
Historic air quality conditions recorded by the US embassy in China based on the
concentration of PM2.5 are most commonly reported through the Air Quality Index (AQI) which
categorizes the state of air quality on a 0 to 500 scale from good to hazardous. According to the
embassy data, between 2008 and 2014, hourly readings of PM2.5 concentration recorded at least
1,812 days of AQI readings which were at least “unhealthy” leading to concern for the exposed
residents (1). Sources of PM2.5 as well as other air pollutants have been linked to major
contributors such as road dust, motor vehicles emissions, and coal burning, both within the city
limits and from industries outside Beijing, leading to a relationship between aerosol-producing
activities and the effect on local AQI (2). Furthermore, long term effects on citizen health due to
exposure to PM2.5 have estimated mortality rates of 5100 individuals per year in Beijing from
the period of 2001 to 2010 caused by PM2.5 related illnesses such as respiratory complications,
highlighting the seriousness of this issue (3).
On top of the point-based sensing techniques implemented by organizations such as the
US embassy in Beijing, currently several models have been developed concerning the
geographical distribution of air quality and PM2.5 through the integration of multiple land based
sensors providing a network of air quality predictions across the area over time. These models
are, for the most part, derived from ground based monitoring data which each cover individual
points throughout the country of China, creating a network-like map through interpolating data
points and climate inputs. This methodology is in some respect limited based on the number of
sensors available as well as the accessibility of the data depending on the party owning them.
Satellite data on the other hand, is publically available and can fill in some of the gaps where
sensors are not located on the ground, creating a more comprehensive perspective of the
geographical distribution of smog-related particles and their relation to the environment.
Literature Review
Leading data sources relating air quality in the city of Beijing that are accessible to the
public include the US embassy’s PM2.5 concentration data, though the data itself has not been
fully validated and is considered preliminary (4). Additionally, this data is based on a single site
in Beijing, and cannot be used by itself to construct a map of PM2.5 concentration’s
geographical distribution. NASA’s Aerosol Robotic Network (AERONET) works similarly by
using multiple ground based sensors across countries like China to determine the amount of
incoming solar radiation which can be compared with the known true solar radiation reaching the
earth to calculate the aerosol optical depth (AOD) or portion of optical depth than is restricted
due to aerosol concentrations such as PM2.5. Although AERONET data can be used in
conjunction with climate dependent variables like boundary layer height and relative humidity to
produce a measurement of PM2.5 air quality data over the area covered by their sites, this map
too is limited in the number of available AERONET sites; currently only two operational sites
exist within the city of Beijing itself (5).
Other air quality ground based sampling sites which exist within the country of China
include a reported 1500 sites creating China’s own national system in monitoring PM2.5 as well
as several other pollutants in the atmosphere (6). This data however is, for the most part, not
publicly available due to download restrictions of the official Chinese air quality reporting
system, and must be supported by third party sensors such as those at the US embassy in Beijing
in order to model geographic air quality distributions.
Overall, it appears that ground based monitoring of PM2.5 and AOD are either limited by
insufficient sourcing sites or inaccessibility to concentration data as much of the data is restricted
from the public view. Even though geographical distribution models can be determined using the
accumulated air quality data that is publicly available, the air quality picture generated through
using the ground based sensors is not fully detailed.
Methodology
The application of remote sensing by satellite monitoring of local air quality for aerosol
pollution is based on the relationship between PM2.5 and AOD. The basic relationship is based
on the amount of radiation is scattered and absorbed by aerosol particles in the atmosphere. This
diminishes the amount of sunlight reaching the earth surface, and can be compared with the
known solar radiation reaching the earth to create a function for AOD proportional to the
concentration of PM2.5. Using this relationship, columnar AOD measured through satellite
sensing can be used to estimate ground-level fine particulate matter (PM2.5) at an accurate rate
(3), to then be applied on a regional scale to create a map of the geographical distribution of
PM2.5 and its effect on air quality and human health.
A multitude of earth
orbiting devices currently exist
gathering imaging data. NASA’s
Moderate Resolution Imaging
Spectro-radiometer (MODIS), at
an LEO orbit of 705 km,
achieves a 2,330 km swath and is
able to provide both land and sea
coverage every one to two days
(7),
allowing
for
many
applications such as the MODIS
aerosol product used to study
sources and sinks of specific
aerosol types on both land and
sea surfaces. MODIS is capable
of a spatial resolution of 500m to
1000m depending on bandwidth
(7), ideal for covering 16,800
km2 of area covered by the city
of Beijing, and could be used as
a source of public archived data
for the history of air quality in
Beijing over the past decade as
well as in the future. Sample data
imaging of aerosol spatial
distribution
developed
by
MODIS over the ocean can be
seen in Fig. 1 (8). MODIS
coverage over land can be used
through retrieval algorithms to
get from AOD to PM2.5
concentrations. These PM2.5
concentrations after being tracked by both time, and geographical distribution, can then be
compared with concentrations recorded by ground sensors such as AERONET. The two possible
outcomes after achieving this satellite data would its agreement with the existing AERONET
data, where the two could supplement each other, or a disagreement in values which would
require further analysis.
Expected Results
Advantages of MODIS aerosol product data include its ability to create a full spatial scan
of the distribution of AOD across an area which ground sensors alone can only model through
integrating a limited number of discrete points. We would expect MODIS to correlate on the
whole with the geographical air quality distribution models already in effect, but also to
contribute through new data to supplement locations where no sampling devices are available for
public access. When used alongside AERONET and US embassy data who are advantageous as
well through their ability to record readings hourly regardless of time of day or cloud cover, a
fuller understanding of both the historical and future spatial distribution of PM2.5 and air quality
will be available for presentation to the Chinese government who may choose to consider these
readings against their own private data.
Timeline
Task 1: Data Gathering
1.1 AERONET data archives
1.2 MODIS data archives
Months
1
2
3
4
5
6
Task 2: Data Analysis
2.1 Retrieval of PM2.5 from AERONET AOD
2.2 Retrieval of PM2.5 MODIS AOD
2.3 Comparisons between AERONET and MODIS
Task 3: Final Report
3.1 Creation of new PM2.5 map
3.2 Presentation to Chinese government officials
Conclusion
The sum of the information gathered by various remote sensing data techniques is able to
create a sense for the overall geographic distribution of PM2.5 across the densely populated area
of Beijing, China. By using the advantages of satellite sensing technology such as MODIS whose
ability to cover both wide and specific areas to compensate for the lack of ground based sensing
monitors, a fuller appreciation for the state of air quality in Beijing could be achieved for public
use, both for the residents in Beijing and for the Chinese government to compare with their own
results.
It should be highlighted that this publically available satellite column based data
gathering of AOD is limited by its own inability to perform its function without daylight and
during periods of dense cloud cover and should be supplemented itself by ground based sensors
such as AERONET and the US embassy in Beijing through their ability to record hourly
readings of PM2.5 concentration and AOD.
References
1. “Six years of Beijing air pollution summed up in one scary chart” Quarts. Web.
http://qz.com/197786/six-years-of-bejing-air-pollution-summed-up-in-one-scary-chart/.
2. Sun, Y., Zhuang, G., Wang, Y., Han, L., Guo, J., Dan, M., Zhang, W., Wang, Z., &
Hao, Z,. (2004). The air-borne particulate pollution in Beijing—concentration,
composition, distribution and sources. Atmospheric Environment, 38:5991–6004,
doi: 10.1016/j.atmosenv.2004.07.009
3. Zheng, S., Pozzer, A. C., Cao, C. X., & Lelieveld, J. (2015). Long-term (2001–2012)
concentrations of fine particulate matter (PM2.5) and the impact on human health in
Beijing, China. Atmos. Chem. Phys., 15:5715–5725,
doi: 10.5194/acp-15-5715-2015
4. “Air Quality Data Files” StateAir. Web.
<http://www.stateair.net/web/assets/USDOS_AQDataFilesFactSheet.pdf>.
5. “AERONET Site Information Map Interface” AERONET. Web.
<http://aeronet.gsfc.nasa.gov/cgi-bin/site_info>.
6. Rohde R. A., & Muller R. A. (2015). Air Pollution in China: Mapping of Concentrations and
Sources PLoS ONE, 10(8): e0135749,
doi:10.1371/journal.pone.0135749
7. “Specifications.” MODIS. Web. <http://modis.gsfc.nasa.gov/about/specifications.php>.
8. “Sample Images.” MODIS Atmosphere. Web. <http://modisatmos.gsfc.nasa.gov/MOD04_L2/sample.html>.
9. Feng, X., Li, Q., Zhu, Y,. Hou, J,. Jin., L., & Wang, J. (2015). Artificial neural networks
forecasting of PM2.5 pollution using air mass trajectory based geographic model and
wavelet transformation. Atmospheric Environment, 107:118-128,
doi:10.1016/j.atmosenv.2015.02.030
10. Hoff, R. M., & Christopher, S. A. (2009). Remote Sensing of Particulate Pollution from
Space:
Have We Reached the Promised Land? Air & Waste Manage. Assoc., 59:645– 675,
doi: 10.3155/1047-3289.59.6.645
11. Thurston, G. D., & Spengler, J. D. (1967). A quantitative assessment of source contributions
to inhalable particulate matter pollution in metropolitan Boston. Atmospheric
Environment, 19:9-25,
doi: 10.1016/0004-6981(85)90132-5
Budget and Justification
Salaries and fringe benefits for both the principle investigator and one graduate student
were based on values dictated by the university applied to half a year. Since no field work or
special equipment is necessary for this project, a timeline of 6 months versus a full year was used
for supervising progress where the major components of the project include data analysis and
presentation of findings.
Proposal Budget
15-Jun
1 Salaries
Principal Investigator
Graduate Students (1)
Ac. Sem.
Stipend
Summer
Stipend
Health
Insurance
GRA
exclusion
2 Fringe Benefits
AY
E
1250
summer E
1250
Base
Salary
10,000
/mo
10,000
/mo
Dec-15
Ac. Yr
0.25
summer
0.25
Ac. Yr.
3
E
12,662
summer
3
E
4,225
Health Ins.
6
E
1,400
E
7375
rates
Ac. Sem.
Stipend
0.375
0.375
468.75
468.75
tuition
Ac. Yr.
29,500
25,324
summer
health ins.
8,449
2,800
1
1.125
Exclusions
GRA Exclusion
Health
Insurance
7375
subtotal
Total Yearly Cost
1,400
8775
20,324
Project Total
11,549
% of
tuition
# grad
students
0.50
1