Monitoring Spatiotemporal Distribution Characteristics of Air
Pollutants in Tianjin by Satellite Remote Sensing
Chen Ziyi
a
Southwest Forestry University, Tianjin, China
Keywords: Satellite Remote Sensing, Air Pollutants, Point of Information.
Abstract: In recent years, science and technology sustainably develop, Satellite Remote Sensing technologies are widely
used in many aspects. Moreover, Satellite Remote Sensing can be more efficient, convenient and objective to
monitoring various environments by electromagnetic radiation. While the air environment condition become
more worse than before, so people pay more attention to air condition and atmospheric environment
monitoring by remote sensing become the main way. This study analyses the possible relationship between
air pollutants and POI data. The experiment was to assess effects of human activities on atmospheric
environment and provide some advice for human activities in the future. What is more, in this research,
utilizing obtained data of air pollutants and point of information data analyse their Spatiotemporal distribution
characteristics and mathematical relation in order to get some objective evidence. In conclusion, the results
show that POI data is correlated but different from the distribution data of different air pollutants.
1 INTRODUCTION
According to the atmosphere pollutants correct
interpretation from China government departments:
In line with the definition provided by the
International Organization for Standardization, air
pollutants usually refers to that some material enter
the atmosphere with enough concentration and time
because of human activities and nature phenomena,
hence it endangers people's comfort level, health,
welfare and Living environment. It is important that
presents serious environmental problems and
endangers people's physical condition (Bergmann et
al, 2020), it includes multiple diseases such as heart
disease, greenhouse effect, traffic delays, upper
respiratory tract infections，etc. In order to effective
control air pollutants and how to solve the above
problem, it is necessary to long-term monitor at first.
At present, there are two ways to detect air gas, one is
the direct sampling method, and the other is
concentration sampling method (China Patent No.
CN201110050054.5, 2012). Aerosols monitoring
technologies include weight method, β radiation
absorption method and Tapere Element Oscillating
a
https://orcid.org/0009-0007-3662-3364
Microbalance (Fu et al, 2011). However, as said
above, all the methods are surface sampling, so they
are always influenced by air gas data quality,
monitoring equipment quantity and quality, sampling
position and sampling range. By contrast, Satellite
Remote Sensing technologies have many advantages,
including real-time, high efficiency, not limited by
time and space, and also can distinguish various kinds
of gas. Meanwhile, Satellite Remote Sensing can
continuously monitor change of pollutants status of
atmospheric gas and water body, forecast
environmental quality, effectively enlarge the
environment of monitoring area and improve the
ability of data acquisition, processing, transmission
and application (Wang et al, 2011), so this study
analyze atmospheric environment condition with
Satellite Remote Sensing data.The sphere and
intensity of human activities has expanded rapidly
because modern society follows the rapid
development of science and technology, which has
brought great harm to the ecological environment.
This study will combine different types of human
conditions by point of information with air pollutants
distribution diagram to analyze the relationship
between the two. After that, this study also explores
216
Ziyi, C.
Monitoring Spatiotemporal Distribution Characteristics of Air Pollutants in Tianjin by Satellite Remote Sensing.
DOI: 10.5220/0013036000004601
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy (IAMPA 2024), pages 216-222
ISBN: 978-989-758-722-1
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

air pollutants spatiotemporal distribution relationship
by visual interpretation method and look for a
connection between air pollutants and human
activities. Ultimately, the study result can improve
some reference information for future environmental
protection implementation measures, prospect of
human development and range of human activity.
2 METHODOLOGIES
Air pollutants consist of two parts: particulate and
gaseous, which is including but not limited to sulfur
compounds, oxides of carbon, nitrogenous
compounds, aerosol, especially fine particles contain
a lot of toxic and harmful substance. This study
utilizes software of ArcGIS and Envi to process POI
data and different stages satellite images of air
pollutants distribution, which will be used to research
air pollutants spatiotemporal distribution
characteristics and the relationship among seasons,
years and human activities in centre of Tianjin.
2.1 Study Area
According to the public information provided by the
Tianjin Municipal People's Government, where it is
located in north China, north China Plain, the total
area is 11966.45 square kilometres and a coastline of
153.669 kilometres, multi ports. Among them, the six
districts led by Heping District are the central urban
areas, which is place of origin and also the centre of
economy, politics, culture and education. This city is
bounded to the east by the Bohai Sea as well as the
north by Yanshan Mountain and it is a transition zone
of coastal plains and mountains. What is more,
Tianjin weathers are more influenced by oceanic
climate, which is for distinct seasons and belong to
warm temperate sub-humid monsoon climate. Tianjin
is rich in foreign trade and has a mature and advanced
maritime transport because it borders the well-
developed capital of Beijing. Be affected by
economy, tertiary industries are always in the leading
position across the whole of China. At present, the
total resident population reached 13.64 million. As
can be seen from the above, this region is
characterized by complex economic conditions and
human activities. Meanwhile, there are various
natural factors, such as wetlands, forest, rivers, over
eighty kinds of fish speciesm and other creatures.
2.2 Data Sources and Experimental
Methods
Table 1: Data Information (Picture credit: Original)
Datasets Format Source
Air
pollutants
NetCDF
The website of China
National Qinghai-Tibet
Plateau Tibetan Plateau
Data Cente
r
POI
Point
features
AMap Services, China
2.2.1 Data Information of Pollutants
All the remote sensing data used in this paper stem
from China authoritative and open data center website
that produced by Wei, J and Li, Z. In this
investigation, the images from distant observation of
the above four pollutants are regionally cropped using
ArcMap tools, and then the data information of the
studied region is obtained.
2.2.2 POI Data
The data of POI is derived from Amap service
system. The Kernel Density Estimation of ArcGIS
was applied to process the relevant POI data (land use
type) in Tianjin and obtain the density map of data
points. The density map will be an indicator of human
activity conditions. All kinds of POI data should be
analyzed in spatial distribution at first and convert
Excel tables to a document of csv format for
vectorization, then generate an accurate shp
document with coordinates. Next step is importing
the document into the program and completing the
analysis to get a map.
2.3 Data Analysis
This stage operation methods of Creating Fishnet and
Extracting Multi Values to Points, which can acquire
digital data from 4 kinds of air pollutants and 3 kinds
of POI raster data. With the extracted values, data
should be imported Excel table, because it is
convenient to preliminarily analysed and use data.
Data eventually is imported Statistical Product and
Service Solutions with a way of Spearman to explore
the potential relationship and influences between POI
and atmospheric contaminants.
Monitoring Spatiotemporal Distribution Characteristics of Air Pollutants in Tianjin by Satellite Remote Sensing
217

3 ANALYSIS AND OUTCOME
3.1 Temporal Analysis
The variation atmospheric contaminants
characteristics of spatial and temporal were analysed
by using different time series data, and the trend of
pollutant concentration variation in different seasons
and different regions was discussed.
Figure 1: O3 (Picture credit: Original; Data from: Wei, J.,
Li, Z. (2023). ChinaHighO3: High-resolution and High-
quality Ground-level MDA8 O3 Dataset for China (2000-
2022). National Tibetan Plateau / Third Pole Environment
Data Center. https://doi.org/10.5281/zenodo.10477125.)
Figure 2: PM10 (Picture credit: Original; Data from: Wei,
J., Li, Z. (2023). ChinaHighPM10: High-resolution and
High-quality Ground-level PM10 Dataset for China (2000-
2022). National Tibetan Plateau / Third Pole Environment
Data Center. https://doi.org/10.5281/zenodo.3752465).
Figure 3: PM2.5 (Picture credit: Original;Data from: Wei,
J., Li, Z. (2023). ChinaHighPM2.5: High-resolution and
High-quality Ground-level PM2.5 Dataset for China (2000-
2022). National Tibetan Plateau / Third Pole Environment
Data Center. https://doi.org/10.5281/zenodo.3539349. )
Figure 4: SO2 (Picture credit: Original; Data from: Wei, J.,
Li, Z. (2023). ChinaHighSO2: High-resolution and High-
quality Ground-level SO2 Dataset for China (2013-2022).
National Tibetan Plateau / Third Pole Environment Data
Center. https://doi.org/10.5281/zenodo.4641538).
From the above figure of O
3
(Figure 1), most
June values were much higher than December values
from 2013 to 2022, and values has continually risen
from 2013 to 2019, which is from 29.3 to 217.2 in
summer. Only the value from 2022 has decreased to
190.1. The principal reason for this condition is
sufficient chemical reaction. O
3
is produced and high
concentration where the temperature is high enough
and it has more hours of light. The value of O
3
was
148.1 in December 2013, and then the high value of
O
3
dropped significantly to 37.8 in December 2016.
One year later value has increased slightly to 42 in
IAMPA 2024 - International Conference on Innovations in Applied Mathematics, Physics and Astronomy
218

December 2019, after that, the value changed again to
57.3 in December 2022.
From the above figure of PM
10
and PM
2.5
(Figure 2 & Figure 3), most December values were
much higher than June values from 2013 to 2022.
There are several factors that should be responsible
for this. The major reason is that two air pollutants are
produced by the direct emission of tiny particles from
various industrial processes, like daily power
generation and coal burning. Winters in northern
China are particularly cold, the government provides
heating by coal burning, which causes more exhaust
gases with tiny particles to be emitted and two kinds
of contaminant concentration values are higher in
December. PM
10
has been declining in June since
2013, from 175.5 to 58.4. In contrast, the December
values is really complex. First, the high value in
December dropped from 227.8 to 90.6, from 2013 to
2019. In December 2022, the values have increased
to 96.5. The map of 2.5-micrometer Particulate Matte
shows a trend with descending in each June since
2013, from 112.8 to 31.4. Four data of different years
have gone through a process of first increasing and
then decreasing. SO
2
data (Figure 4: SO”) cannot be
interpreted a small and precise region because the
resolution is 10 KM before 2019; therefore, this study
only analyzes data from 2019 onwards. The values for
June and December are not much different from year
to year.
3.2 Spatial Analysis
Figure 5: Traffic and Park of POI (Picture credit: Original)
Figure 6: Business of POI (Picture credit: Original).
As can be seen from the figure the POI data
(Figure 5Figure 5: Traffic and Park of POI & Figure
6) is divided into three categories: commercial,
parkland and transport. The high values of these
three types of data are concentrated in the western
part of the main urban area of Tianjin and spread in
all other directions, but the distribution of various air
pollutants at different times of the year is really
different, so SPSS software was applied for
deciphering to ensure the accuracy of data
correlation. Since the POI data are only available
beyond 2023, the 2022 annual mean data for each air
pollutant were obtained for analysis. Between the
Greenbelt and Parks category of points of interest
and O
3
had a strongest correlation about 0.349 and
they are both increase and decrease, whereas SO
2
had
a negative and smaller correlation with PM
10
; Traffic
points of interest had a strong and negative
correlation with the SO
2
distribution map, but the
association with the distribution of PM
2.5
is positive;
The correlation between points of interest in the
business category and the distribution of each air
pollutant is low. The result based on the data
presented in the table show correlation coefficients
between 0.2 and 0.4 indicating that the individual air
pollutants are weakly correlated with the intensity of
human activities represented by the POI. The
following tables are shown outcomes:
Table 2: Correlation between Park of POI and air pollutants
(Picture credit: Original).
Park
O
3
PM
2.5
SO
2
PM
10
Spearman
Rho
R
.
.349** .315** -.284** -.222**
Sig.
.
.000 .000 .000 .000
N
.
5694 5694 5694 5694
**. <0.01 , The correlation is si
g
nificant.
Monitoring Spatiotemporal Distribution Characteristics of Air Pollutants in Tianjin by Satellite Remote Sensing
219

Table 3: Correlation between Traffic of POI and air
pollutants (Picture credit: Original).
Traff-
ic
O
3
PM
2.5
SO
2
PM
10
Spearm--
an
Rho
R
.
.287** .302** -.303**
-
.222**
Sig.
.
.000 .000 .000 .000
N
.
5694 5694 5694 5694
**. <0.01 , The correlation is significant.
Table 4: Correlation between Traffic of POI and air
pollutants (Picture credit: Original).
Busi-
ness
O
3
PM
2.5
SO
2
PM
10
Spearm-
-an
Rho
R .
.258** .245** -.186** -.167**
Sig.
.
.000 .000 .000 .000
N
.
5694 5694 5694 5694
**. <0.01 , The correlation is si
g
nificant.
Points of interest in the Green Space and Park
have the strongest and positive correlation with the
distribution of ozone and the coefficient of
association is 0.349, while SO
2
has a negative and
smaller correlation with PM
10
.
3.3 Results
The Parks and Green Spaces of points of interest had
the strongest and favorable relevance of O
3
, having a
correlation value about 0.349. This shows that parks
and green spaces concentration distribution is
positively pertinence with O
3
, since the formation of
O
3
is related to its precursor substance (Volatile
Organic Compounds, VOCs) and Nitrogen oxide
(NO
x
). However, the green vegetation discharges the
Biogenic Volatile Organic Compounds (BVOCs) to
accelerate production of O
3
(Bao et al, 2023).
Meanwhile, the stomata on the leaves and stems of
vegetation can absorb and sediment and it indeed has
certain effect of adsorption and conversion on O
3
. As
a consequence, there is a great correlation between
the O
3
concentration and parks, but the study areas are
also influenced by various human activities because
it is the centre of Tianjin.
Among the SO
2
, PM
10
and POI of Parks and
Green Spaces show a trend that is negative and little
correlation, so woodland and grassland play an
important role in reducing, producing and controlling
particulate matter concentrations (Zhai et al, 2022),
which is due to have a function. The special function
can help purify the atmosphere of polluting gases;
therefore, it can also absorb SO
2
. When blades fell to
the dirt, the sulfur-absorbing leaves with in air rot
back into the soil. At the same time, parks or open
spaces, in which only pedestrians and tourists move
about, are essentially free from internal conditions
that generate all types of air pollutants. This has the
effect of making SO
2
and PM
10
concentrations
relatively small.
Traffic points of interest had a strong and
adverse relationship with SO
2
, while 2.5-micrometer
Particulate Matter had a relatively feeble but positive
connection. It is possible that SO
2
and PM
2.5
are
mainly emitted through industry; especially, the coal
burning for heating during the cold in northern China.
However, Tianjin began to run new energy pure
electric buses since 2020. Afterwards, the full use of
such buses, and the government decided that private
cars are restricted in the main urban area of the city
and actively promote using public transport and new
energy vehicles. Hence, SO
2
is negatively correlated
with traffic interest points. According to a document
of China Mobile Source Environmental Management
Annual Report (2023) released by China's Ministry of
Ecology and Environment, the operation of gasoline-
driven vehicles still produces a lot PM
2.5
, So the
analysis results is positively correlated.
Commercial POI has little relevance to other 4
kinds of air pollutants, correlation coefficient is
between 0.2 to 0.3. This means that there is a weak
impact of commercial activities on air pollutants, due
to the dispersed source of pollution. Apart from that,
different types of business have a wide variation in
impact for air pollutants.
4 CONCLUSIONS
The relationship between various POI, such as parks
and green Spaces, traffic and commerce, and air
pollutants in Tianjin were analyzed by SPSS, O
3
concentration increased with the rising of the number
of parks and green Spaces. It is possible that
biovolatile organic compounds (BVOCs) released by
plants and produce ozone in sunlight. However, O
3
concentration was influenced by the adsorption and
transition abilities of stomata. So it is necessary to
balance the relationship between green space
expansion and air quality in future urban plannings.
Secondly, the study found that parks and green
Spaces had a negative correlation and little effect on
SO
2
and PM
10
concentrations. This is mainly due to
the fact that plants have a self-purification function of
vegetation, it can effectually reduce SO
2
and
particulate matter concentrations. In addition, forests
and grass lands not only adsorb SO
2
, but also can take
sulfur back to lands when plants rot. To conclude, the
IAMPA 2024 - International Conference on Innovations in Applied Mathematics, Physics and Astronomy
220

construction of urban green space helps beautify the
environment as well as improves air quality to a
certain extent.
In terms of transportation POI, the correlation
between SO
2
and traffic POI is strong and negative,
because Tianjin government promoted new energy
buses and restricted the use of private cars in recent
years. Although the use of new energy buses has
reduced SO
2
emissions, gasoline-powered vehicle
still produce lots of PM
2.5
. To sump up, the
government should durably execute this policy and
improved the relevant laws.
Then, the correlation between commercial
points of interest and various types of air pollutants is
weak.
The reason why the different types of business
activities have different contribution values for air
pollutants distribution. To summarize, the results
need further detailed analysis because of
indeterminacy.
Finally, the study revealed the complex
relationship between different POI and air pollutants
in Tianjin and also supplied some scientific advice for
civic plannings and environmental protection.
Ultimately, these recommendations can lead to
sustainable urban development and environmental
improvement.
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