Urban Climate 41 (2022) 101070
Contents lists available at ScienceDirect
Urban Climate
journal homepage: www.elsevier.com/locate/uclim
Analysis of spatial and temporal distribution and influencing
factors of fine particles in Heilongjiang Province
Jiemei Liu , Xiuyan Gao , Zhaohui Ruan , Yuan Yuan *, Shikui Dong
Key Laboratory of Aerospace Thermophysics, Ministry of Industry and Information Technology, Harbin Institute of Technology, 92 West Dazhi Street,
Harbin 150001, China
A R T I C L E I N F O
A B S T R A C T
Keywords:
Fine particulate matter
Mean radiant temperature
Meteorological conditions
Social development factors
Temporal and spatial distribution
Based on fine particulate matter (PM2.5) data obtained from the Ministry of Environmental
Protection of China, this study focused on analyzing the pollution situation, trends, and pollution
frequency changes in the urban area of Heilongjiang Province from February 2015 to April 2019.
We also used and analyzed the social emission source list and human comfort index data to
examine the potential relationship between the above factors and changes in air quality. And we
used the Spearman correlation coefficient to explore the correlation between groups of variables.
This study quantified the impacts of social development factors and meteorological parameters on
PM2.5 and the INDEX which is a comprehensive consideration of the evaluation indicators (social
economy, PM2.5, living environment comfort) through the entropy method and stepwise regres­
sion analysis. The key findings included the following: (1) The pollution period in Heilongjiang
Province was from October to February, and Harbin had the longest annual pollution period. (2)
The PM2.5 concentration was weakly correlated (+) with the energy consumption of industry
enterprise (ECIE) and temperature (T). (3) The most significant contributors to INDEX was
number of automobiles per 100 Urban Households, which reflected that AUH can be controlled to
achieve the goal of reducing PM2.5 concentration on the premise of ensuring stable social and
economic development and reducing air pollutants to human health. This showed that social
activities played an important role in the economy, air quality, and living environment comfort.
This study is of great significance to urban environmental governance.
1. Introduction
Fine particulate matter is an aerosol with an aerodynamic diameter below 2.5 μm (PM2.5), which is mainly due to human activities
such as urbanization and industrialization. China is one of the fastest developing country worldwide. However, the environmental
quality and ecological conditions faced in the process of urbanization are under tremendous pressure. Coal-based energy consumption
and the number of motor vehicles have increased sharply. and numerous harmful substances, from industrial production have been
discharged into the atmosphere, which has severely affected the air quality of cities (Ruili et al., 2019). Urbanization development
causes environmental problems in cities (Shi et al., 2020), and population pressure. Residents have been exposed to the outdoor
environment for a long time, which has attracted a lot of attention to the outdoor microclimate of the city (Du et al., 2020a). Thermal
comfort plays an unparalleled role in evaluating the outdoor environmental quality (Roshan et al., 2019; Rupp et al., 2015). Related
* Corresponding author.
E-mail address: yuanyuan83@hit.edu.cn (Y. Yuan).
https://doi.org/10.1016/j.uclim.2021.101070
Received 20 June 2021; Received in revised form 10 December 2021; Accepted 19 December 2021
Available online 31 December 2021
2212-0955/© 2021 Elsevier B.V. All rights reserved.
Urban Climate 41 (2022) 101070
J. Liu et al.
studies claim that extreme heat and cold can fatal to an extent for any organism, especially for humans (Li and Liu, 2020). And,
research shows that there is a direct link between extreme temperature and air pollution (Pascal et al., 2021). Between 51 and 81% of
heat wave days had ozone concentrations higher than 120 μg/m3. With 0–38% of cold spell days associated with PM10 concentrations
higher than 50 μg/m3. Among the air pollutants, particulate pollutants have a greater impact on the quality of life and health of
residents, especially fine particulate matter (S. EPA, 1999). Studies have shown that the harm of particulate matter is mainly related to
the particle size and its concentration. The smaller the particle size, the deeper it can enter the human body, thereby causing harm to
the human respiratory tract, lungs, and cardiovascular system. At the same time, some bacteria, microorganisms and viruses will be
adsorbed on the particles, causing harm to the human body. According to the 2010 Global Burden of Disease Study (Zou et al., 2017),
more than 1.2 million people died from exposure to high levels of PM2.5 from 1990 to 2010, which was 38% of the total deaths caused
by global particulate pollution. In addition, every 10 mg/m3 increase in the concentration of fine particles in the air increases the risk
of lung cancer death by 9%. As an important part of air pollution in China, understanding the temporal and spatial distribution and
dynamic changes of PM2.5 can guide the coordination of urban development and air quality in China, and help reduce the adverse
health risks of personal exposure to particulate matter and improve the livability of residents.
There are currently a variety of calculation models available for urban microclimate applications (1-4 m), such as RayMan (Sal­
amOthman and AhmadAlshboul, 2020), FLUENT (Gaber et al., 2020), OpenFOAM (Wong et al., 2021) and Star-CCM+ (Botham-Myint
et al., 2015). In this type of research, ENVI-met is widely used due to the balance between its model complexity, user-friendliness and
lower computational cost. ENVI-met is a computational fluid dynamics (CFD) model that relies on the RANS equation to solve the
atmospheric flow and heat transfer in the urban environment (Crank et al., 2018). Sinsel et al. (Sinsel et al., 2021). used weather
research and prediction models (WRF) and ENVI-met to compare the cooling effects of (super) cool roofs in two cities using mesoscale
and nested microscale simulations, respectively. The two models show extremely high consistency in the statistical analysis of
simulated pedestrian horizontal air temperature and roof surface temperature. In order to improve the thermal comfort of the city,
Mohammad et al. (Mohammad et al., 2021). used ENVI-met to evaluate the parameters (temperature (T), surface temperature, wind
speed (Ws), mean radiation temperature (Tmrt, ◦ C) and physiological equivalent temperature) and their relationships in different
scenarios. The results show that higher tree coverage results in better thermal comfort, and it also enhances PET for pedestrians along
the street during the day.
Due to the impact of PM2.5 pollution on human health (Naghavi et al., 2017; KamalJyoti et al., 2018; Tan et al., 2020), air quality,
and atmospheric climate systems (Huang et al., 2014), pollution has become a global problem (Hou et al., 2019). The quality of urban
environmental air quality reflects the degree of air pollution, which is mainly determined by the mass concentration of various pol­
lutants in the air. Man-made pollution emissions (Sabrin et al., 2020; Cass, 1998) are among the most important factors affecting air
quality, and geographical location and meteorological factors are also worthy of attention. It has been reported that due to the
exponential growth of urban populations, densely populated areas are usually affected by extreme heat events and higher air pollution
levels (Guo et al., 2015; Kotharkar and Surawar, 2016). Cities with industrial development as the main economic construction have
more severe environmental degradation (Son et al., 2020; Gao et al., 2020; Wang et al., 2012). For this reason, much effort has been
devoted to the physical, chemical, and optical properties of atmospheric PM2.5 (Zhao et al., 2019; Zhang et al., 2018; Liu et al., 2021).
The study on the chemical composition of aerosols in PM2.5 particles reported that Ca2 + is the basis of the dust aerosols, and the
maximum concentration, 6.57 μg/m3, in the spring is associated with the frequent dust events. And the highest Cl− , 10.37 μg/m3, of
the winter is mainly from coal (Zhang et al., 2018). These studies play an important role in understanding the changes, formation
mechanisms, and aerosol sources in China. However, the urban objects of this kind of research are mainly done for first- and secondtier cities distributed in the warm temperate zone and subtropical zone (Wang et al., 2019; Chen et al., 2020; Lv et al., 2021; Liu et al.,
2020), along with a few reports in Northeast China. Heilongjiang Province is a typical industrialized province in the severely cold
region of Northeast China. Urbanization and agricultural development are factors that cannot be ignored, as they cause environmental
pollution. Judging from China’s provincial-level data collected on the platform of the national environmental protection agency, in
2017, China’s air pollution control has generally completed the preliminary emission reduction plan of the pollution control action
plan. In Heilongjiang Province, which is an old industrial zone, the PM2.5 concentration not only did not decrease but increased, and
this rebound phenomenon ranked among the top ten provinces. Therefore, the establishment of effective mechanisms in old industrial
areas has attracted more and more attention from environmental management departments and scholars (Xue et al., 2020).
Heilongjiang Province (121◦ 11′ E-135◦ 05′ E, 43◦ 26′ N-53◦ 33′ N) is located in the northeastern part of China. A humid continental
climate (Köppen Dwa or Dwb) predominates in the province, though areas in the far north are subarctic (Köppen Dwc) (Wikipedia,
2020; File:Asia Köppen Map.png, 2020). The main characteristics of the province’s climate are low temperature and drought in spring,
warm and rainy summer, easy waterlogging and early frost in autumn, cold and dry winter, controlled by the Mongolia-Siberian high
pressure, and prevailing sinking airflow climate. Westerly wind prevails throughout the year in Heilongjiang Province, southwesterly
wind prevails in the right bank of Songhua River, and northwesterly wind prevails in the west and north. Northwest winds are frequent
in winter. In summer, the southerly wind prevails in the south and the northeast wind prevails in the northeast. The wind direction is
similar in spring and autumn, with more southwesterly winds in the south and center, and more northwesterly in the north. Due to the
dry surface, sand and dust weather is prone to occur, and sand storms often occur. Controlled by cold and high pressure in winter,
atmospheric stratification is relatively stable, and it is easy to form an inversion layer, and pollutants are not conducive to diffusion.
Previous studies of Chinese cities have shown that human activities and natural factors may affect the concentration of air pol­
lutants. For example, Gao (Gao et al., 2019) and Guo (Guo et al., 2019) found that urban morphology has a significant impact on PM2.5
concentration and urban planning factors have an impact on pollution exposure in Shanghai and Wuhan, respectively. In addition,
Wan (Wan et al., 2020) revealed that daily traffic accidents are positively correlated with PM2.5. In a study of 58 cities in northern
China, Cai (Cai et al., 2020) found that winter heating in provincial capitals has a greater impact on air pollution than non-provincial
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J. Liu et al.
capitals. Regarding Beijing, relative humidity and wind speed are the main meteorological factors affecting the distribution of PM2.5
(Zhang et al., 2020). Yang (Yang et al., 2021) showed the influence of the meteorological lag mode on the PM2.5 concentration in China
that the influence of rainfall and wind was delayed and lasted for 2–4 days. Jin (Jin et al., 2019) and Zou (Zou and Shi, 2020) also
reported similar findings. The level of urbanization is closely related to PM2.5, and Cheng (Cheng et al., 2017) found an inverted Ushaped curve between the level of economic development and China’s air pollution. Considering the relationship between the change
in PM2.5 concentration and environmental factors in the Yangtze River Delta through the refined spatial scale, researchers reported
that the contribution of natural factors to the change in PM2.5 concentration is greater than socioeconomic factors (Xu et al., 2020a).
In summary, social and meteorological factors are closely related to the concentration of air pollutants. However, few studies have
considered the impact of social development, meteorological factors, and living environment quality on air pollution, especially in the
Northeast.
This study investigated the meteorological factors in Heilongjiang Province, various social development indicators, the distribution
characteristics of PM2.5 concentration, and human comfort. The air pollution levels and time-space changes in different regions and
periods were also analyzed. A stepwise regression analysis of the evaluation indicators (The PM2.5, human comfort, and socioeconomic
indicators) and factors (Remaining social development factors and meteorological factors) was carried out to explore the factors that
make a significant contribution to the evaluation indicators. Furthermore, to strengthen the prevention and control of regional
pollution, while ensuring the stable development of society and economy and reducing the harm caused by air pollutants to human
health and the environment, this study reported about the sensitivity factors under the comprehensive consideration of three in­
dicators. The results of this research may be valuable for city planners to develop successful planning strategies to solve the problems of
urbanization, livability, and quality of life.
2. Data and methods
2.1. Data collection
This study uses continuous and real-time measurement data of PM2.5 mass concentration in cities in Heilongjiang Province from
February 2015 to April 2019, derived from the air quality online monitoring and analysis platform (Air Quality Online Monitoring and
Analysis Platform, 2020), provided by the Ministry of Ecology and Environment of China (Ministry of Environmental Protection,
2020). For more detailed information on the measurement methods of pollutant concentration, uncertainty, and accuracy (S. EPA,
1999), please refer to the China Environmental Protection Standard HJ 653–2013 (China Environmental Protection Standard HJ 6532013, 2013).
In addition, the Aerosol Optical Depth (AOD) dataset retrieved by MODIS is used as auxiliary data to support and ensure the
analysis results of the PM2.5. In this study, a 3-level monthly average AOD with a resolution of 1◦ × 1◦ obtained from the MODIS sensor
on the Terra satellite using the dark blue (DB) algorithm was downloaded from the NASA website(Atmosphere Discipline Team Imager
Products, 2020). After correcting and analyzing the data using ENVI (ENVI User Manual, 2020) and ARCGIS 10.6 (Geoprocessing
Tools, 2020), the annual average AOD statistics of Heilongjiang Province in 2015 and 2017 were obtained to study air pollutant
changes in both periods.
The meteorological and soil data of various cities in Heilongjiang Province used in this study were obtained from the daily dataset
of China’s surface climate data and the near-real-time product dataset of the China Meteorological Administration’s land data
assimilation system (CLDAS-V2.0), provided by the China Meteorological Data Network (National Meteorological Science Data Center,
2020; Qiu et al., 2019; Li et al., 2017a). Meteorological data included rainfall (Rf), Ws, T, relative humidity (Rh), and wind direction
(Wd), soil temperature, and soil humidity. For more detailed information on its uncertainty and data processing methods, please refer
to Meteorological data set documentation (Meteorological Data Set Documentation, 2020), CLDAS atmospheric drive field product
V2.0 (CLDAS Atmospheric Drive Field Product V2.0, 2020), CLDAS soil temperature analysis product V2.0 (CLDAS Soil Temperature
Analysis Product V2.0, 2020) and CLDAS soil relative humidity analysis product V2.0 (CLDAS Soil Relative Humidity Analysis Product
V2.0, 2020).
To investigate the impact of PM2.5 emissions in Heilongjiang Province and the interaction between the social environment, we used
the data published in the “Heilongjiang Statistical Yearbook” (the social emission source inventory) (Heilongjiang Bureau of Statistics,
2020), including the energy consumption of industry enterprise (ECIE), population density of urban area (PDUA), floor space of
buildings under construction (FSBU), number of automobile per 100 Urban Households (AUH), number of passengers carried by bus
(PCB, trolley bus), quantity of heat supplied hot water (HSHW), and gross domestic product (GDP). The study area involves 12
prefecture-level cities in Heilongjiang Province. Statistical yearbook data has been widely used in scientific research (Xia et al., 2020;
Li et al., 2018a; Shi and Wu, 2020; Fan et al., 2020). For more detailed information about the data statistics system and indicator
interpretation, please refer to National Statistical System (National Statistical System, 2020) and Index explanation (Index Explana­
tion, 2020).
2.2. Research methods
To explore the correlation between the influencing factors of fine particles and the factors that contribute significantly to air
pollution, we first inspected the temporal and spatial distribution of air pollution data to obtain a preliminary understanding of the
pollution levels in various regions. Furthermore, using IBM SPSS Statistics (SPSS Statistics Document, 2020), a response module and a
parameter module were established to analyze the correlation between variables and the influence of the parameter module on the
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J. Liu et al.
response module. The response module consists of PM2.5, Tmrt, social economy, and comprehensive consideration of the three in­
dicators (denoted as INDEX). Among them, INDEX is a comprehensive evaluation index obtained after entropy processing on three
evaluation indexes (PM2.5 concentration, human comfort, and socioeconomic indexes). Its significance is to reduce the harm caused by
air pollutants to human health and the environment while ensuring the stable development of society and economy. The parameter
module includes social development factors and meteorological parameters. A human body comfort model and a significant sensitivity
factor model were established to obtain Tmrt and significant contribution factors, respectively. For the specific solution process, please
refer to subsections 2.2.1 and 2.2.2. The detailed research process is shown in the flow chart of the analysis of the temporal and spatial
distribution of fine particles and influencing factors (Fig. 1).
2.2.1. Human body comfort model
Tmrt is an important meteorological variable that controls human energy balance and outdoor thermal comfort. It describes the
exchange of short and longwave radiation between humans and the surrounding environment, that is, radiant heat load (Wallenberg
et al., 2020), which shows how humans experience the radiation of the surrounding environment (Manavvi and Rajasekar, 2020).
Studies have shown that, compared with traditional meteorological indicators, Tmrt can be a good indicator of the difference in thermal
comfort conditions within a city (Mayer and Ppe, 1987). To this end, we used ENVI-met software to obtain the required Tmrt data.
ENVI-met can perform the calculation output of Tmrt, which can comprehensively reflect the influence of sunlight, buildings and
ground, surrounding air, and green plants on a grid in the calculation area, and reflect the longwave effect and shortwave radiation on
the environment in the form of temperature. The incident short-wave and long-wave fluxes at the top of the model were calculated by
two-stream approximation and some empirical formulas (Taesler and Andersson, 1984). The atmospheric radiation budget is defined
by the absorption and emission coefficients of different atmospheres. These coefficients depend on the optical thickness of the at­
mosphere, that is, the number of aerosols and the amount of water vapor, carbon dioxide, ozone, and other greenhouse gases in the
atmosphere. In the model area, the distribution of solar radiation and long-wave radiation is affected by factors such as the ground,
buildings, and plants. The calculation formula for Tmrt is as follows (Huttner, 2012):
)
( (
) ) 0.25
1
αk (
(1)
Tmrt =
Qlw,in + × Qsw− diff ,in + Qsw− dir,in
σ
ε
where the emission coefficient of the human body (ε) is set to 0.97 and αk as the absorption coefficient of the human body for short
Fig. 1. Analysis flow chart of the temporal and spatial distribution of fine particles and influencing factors.
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J. Liu et al.
wave radiation is set to 0.7. σ is the Stefan-Boltzmann constant (Huttner, 2012).
The incoming long wave radiation Qlw, in is assumed to come to 50% from the sky, buildings and vegetation and to 50% from the
ground surface (Huttner, 2012):
(
)
(
)
4
4
4
Qlw,in = 0.5 × vfveg εveg σTveg + vf( bldg εbldg σT)bldg + + 0.5 × σεground Tground
(2)
vfsky Qlw,sky + vfbldg 1 − σ bldg Qlw,sky
The view factors vf give the percentage of vegetation/ buildings/ sky that can be seen from the specific grid point. The physical
correct approach would be to calculate the long wave radiation fluxes based on the emissivity and temperature of the elements within
view. This would however require considerable amounts of CPU time and RAM. Therefore, the average emissivity ε and temperature T
of all plants/ building surfaces within the model area are used instead. The incoming long wave radiation from the sky Qlw, sky is
calculated based on the air temperature, air humidity and some empirical parameters. For long wave radiation coming from the ground
only the emissivity and surface temperature of the grid corresponding grid cell are considered (Huttner, 2012).
The diffuse incoming short-wave radiation Qsw− dir, in is calculated accordingly (Huttner, 2012):
(
)
)
(
vfbldg rfbldg Qsw− dir,sky +
(3)
Qsw− dir,in = 0.5 ×
+ 0.5 × rfground Qsw,ground
vfsky Qsw− diff ,sky
With rf as the reflectivity and Qsw, ground as the overall shortwave radiation at the ground surface of the corresponding grid cell.
The incoming direct short-wave radiation Qsw− dir, in is calculated as the direct short-wave radiation within the grid cell multi­
plicated with a projection factor pf: Qsw− dir, in = pf × Qsw− dir. This projection factor depends on the azimuth angle of the sun ∅[74]:
(4)
pf = 0.42 × cos∅ + 0.043 × sin∅
There are four main steps in ENVI-met microenvironment simulation. First, set the location, grid resolution, nested grid and other
parameters in the background settings. Use its sub-module SPACES to create a model file (*.inx) of the research area by combining the
base map in .BMP format and the underlying surface data information. Secondly, use ConfigWizard to set simulation parameter
conditions (*.sim), such as file path, start time, result output time interval, initial meteorological conditions (temperature, wind speed,
wind direction, relative humidity, etc.), soil data, boundary inflow conditions and other parameters. Then comes the simulation
calculation of ENVI-met. Finally, use its result processing and analysis software Leonardo to display and analyze the simulation results.
Because Tmrt is mainly affected by solar radiation, buildings, ground, surrounding air, and green plants, we selected the heat index
data of the following day (16–24 h, 0–15 h on the following day) of the three typical days of the summer solstice, autumnal equinox,
and winter solstice, and averaged them to represent the annual average Tmrt. After considering social factors, such as ECIE, PDUA,
FSBU, urban traffic volume, HSHW and GDP, latitude, air pollution, and human traffic, the prosperous areas in five cities, including
Daqing, Harbin, Shuangyashan, Heihe, and Suihua, were selected as the research objects. Monde and Spaces controlled the creation of
a regional space model for it, with the assistance of ENVI-guide controls to complete a typical day simulation.
2.2.2. Significantly sensitive factor model
We tested the correlation strength between PM2.5, meteorological parameters, human comfort, and social development factors
using IBM SPSS Statistics 25 to better understand the relationship between them. Specifically, we explored these relationships using
scatter plots. When a non-linear relationship is observed, the variables are transformed, and the Spearman correlation coefficient is
used to characterize the relationship between them. The Spearman correlation coefficient was a quantitative evaluation, which
measures the dependence of two variables, using a monotonic equation to evaluate the correlation of two statistical variables
(Tomaszewska et al., 2020), given by the following formula (Rocha et al., 2020):
∑
4 ni=1 di2
rs = 1 −
(6)
n3 − n
where di is the difference between the two samples and n is the number of samples. To better show the correlation between the
variables, a heat map analysis was applied to the Spearman correlation matrix.
One of the research goals is to determine the sensitivity factor under the response module to prioritize environmental control of this
factor. Stepwise regression analysis is the process of screening variables in the regression analysis. Using this method, a regression
model can be constructed from a set of candidate variables so that the system can automatically identify influential variables (Chen
et al., 2019). The general form of (Noryani et al., 2019) is as follows:
(7)
yi = β0 + β1 xi + ⋯ + εi i = 1, ⋯, n.
R2 =
SSR
SSRe s
= 1−
SST
SST
AdjR2 = 1 −
(8)
SSRe s
dfe
SST
dfT
(9)
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J. Liu et al.
F=
SSR (x2 |x1 )
MSRES (x1 , x2 )
(10)
where yi is the response variable (PS), xi is the explanatory variable, β1, β2, …, βN are the partial regression coefficients, and εi is the
error term. SSR is the regression sum of squares, SSRe s is the residual sum of squares, SST is the total sum of squares, df is the degree of
freedom, F is the statistic, and MSRES is the mean square of the residuals.
Forward selection and backward elimination are the processes of gradual regression. Forward selection has no regression variables
Fig. 2. Average monthly weather conditions on the ground (The cities represented as a–m is Daxinganling, Heihe, Yichun, Suihua, Qiqihar, Hegang,
Daqing, Jixi, Shuangyashan, Jiamusi, Mudanjiang, Harbin and Qitaihe, respectively.) and the location map of each city in Heilongjiang Province.
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Urban Climate 41 (2022) 101070
J. Liu et al.
Fig. 2. (continued).
outside the intercept, and the variables are introduced one by one. After each new variable is introduced, the old variables that have
been selected in the regression model are tested individually. If the F value exceeds the critical value corresponding to a given sig­
nificance level α (FIN), the explanatory variable is added to the model. When the F value does not exceed the FIN, or the last explanatory
variable is added to the model, the process stops (Noryani et al., 2019). The opposite is the backward elimination method. The process
initially includes all explanatory variables in the model, and Fout (the critical value corresponding to a given significance level α) was
used to delete insignificant explanatory variables (Noryani et al., 2019).
We used PM2.5, human comfort, socioeconomic status, and INDEX as response variables, and ECIE, PDUA, FSBU, AUH, PCB, HSHW,
Rf, Ws, T, and Rh as explanatory variables, and performed stepwise regression analysis. Finally, the significant sensitivity factors for
each response variable were obtained.
3. Results
In analyzing the spatial and seasonal complexity of urban PM2.5, Zhao (Zhao et al., 2021) found that when considering northern
Chinese cities, socio-economic variables had the largest contribution to explaining changes in urban PM2.5 concentration, followed by
meteorological variables, and biophysical structure variables have the smallest contribution. In urban PM2.5 concentration changes,
the combined effect of various variables accounted for 44%, and the combined effect of socioeconomic variables and meteorological
variables accounted for 36%. Therefore, in order to explore the significant contribution factors of Heilongjiang Province’s air quality in
meteorological and socio-economic variables, this work first collected air pollution data from 2015 to 2019, and identified the changes
and distribution patterns of air quality in various cities in Heilongjiang Province in time and space. At the same time, the average
radiation temperature and social development factor data were quantitatively analyzed. By analyzing the changing trends of various
parameters, we can roughly find the suspicious meteorological factors and social development factors that lead to the deterioration of
the air. Further, we first used the Sperman correlation coefficient to analyze the correlation strength between various factors (PM2.5
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Urban Climate 41 (2022) 101070
J. Liu et al.
concentration, meteorological parameters, human comfort and social development factors) to better understand the relationship
between them. Finally, the regression analysis method was used to find the sensitivity factors of PM2.5 concentration, socio-economic
indicators and human comfort indicators. The results of statistical analysis will be of great value to urban planning and atmospheric
environment research.
3.1. Regional characteristics of meteorological parameters
Meteorology played an important role in the formation, transportation, deposition and transformation of air pollution. During the
pollution period in the Beijing-Tianjin-Hebei region on December 20–26, 2015, PM2.5 increased by about 34% due to bad weather
conditions (Ma et al., 2020). Research on two haze events on the Chengdu Plain from January 6 to 16 showed that the reduction of the
planetary boundary layer (PBL) height inhibits the spread of air pollutants, while the increase in relative humidity accelerates the
conversion of secondary pollutants (Li et al., 2017b). In the study of the spatial and temporal distribution of particulate matter
concentration and the impact of meteorological parameters on particulate matter, Li et al. (Li et al., 2017c) found that thermal
convection was beneficial to the vertical transportation of dust particles to a certain extent. The special discovery was that the sec­
ondary particles were transformed through photochemical processes under higher temperature conditions, such as summer. In
addition, the research results showed that strong horizontal diffusion played an important role in reducing PM concentration, and
strong winds helped release and transport coarse dust particles from the ground in spring.
This study investigated the average monthly weather conditions (Rf, Ws, T, Rh) on the ground in cities in Heilongjiang Province to
analyze the relationship between these conditions and PM2.5, as shown in Fig. 2. The letters a-m in the subgraph represented Dax­
inganling, Heihe, Yichun, Suihua, Qiqihar, Hegang, Daqing, Jixi, Shuangyashan, Jiamusi, Mudanjiang, Harbin and Qitaihe,
respectively.
In terms of Rf, the rainy-day frequency in each city was between 19.18% and 43.62%, with light rainy days (0–10 mm) being the
majority. In 2017, there was a heavy rainstorm (100–250 mm) in Mudanjiang, Harbin, and Yichun City, and a more severe rainstorm
(≥250 mm) in Jixi City; a heavy rainstorm occurred in Yichun City in 2018. The areas with the highest (lowest) rainfall frequency from
2018 to 2015 were Yichun City (Daqing), Qitaihe (Qiqihar), Daxinganling (Qiqihar), and Yichun (Qiqihar). During the study period,
the two areas with the highest (lowest) occurrence frequency of weather phenomena above the moderate rain level (10–25 mm) were
Yichun/99 (Qiqihar/52) and Hegang/89 (Daqing/57). Light rain and no rain occurred mainly in 2015 and 2017. Compared with other
years, the probability of light rain in 2015 was higher.
In terms of Ws, except for Daxinganling (0.2 m/s-3.3 m/s), the Ws in all regions in 2018 was generally distributed between 1.51 and
3.3 m/s. In 2017, there was a phenomenon where the Ws was below 0.2 m/s in Yichun City. Except for Jixi (3.3–5.4 m/s), the Ws was
generally distributed between 1.51 and 3.3 m/s. In 2016, Harbin and Daxinganling each had a Ws below 0.2 m/s. In addition, Ws were
generally distributed between 1.51 and 3.3 m/s. There were four and three wind speeds lower than 0.2 m/s in Yichun City and
Daxinganling. Except for Jixi (3.3–5.4 m/s), Ws were generally distributed between 1.51 and 3.3 m/s. The areas with the highest
Fig. 3. Monthly change of average PM2.5 concentration in Heilongjiang Province.
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J. Liu et al.
(lowest) occurrence frequency of weak wind levels (0–1.5 m/s) from 2018 to 2015 were Daxinganling, Yichun, and Hegang (Jixi,
Qitaihe, and Daqing). The area with the highest (lowest) occurrence frequency of strong winds (>10.7 m/s) above this level was Jixi
City (Harbin, Daqing, and Heihe). The probability of the monthly average wind speed in Heilongjiang Province being below 3.3 m/s
was 81.67%, while the probability of occurrence from October to February was 92%.
If we analyze the T distribution from the perspective of regional differences, we found that the areas with the highest (lowest)
occurrence frequency above the thermal level (>21.9 ◦ C) in 2015–2018 were Daqing, Qiqihar, and Harbin (Daxinganling, Hegang,
Yichun). From 2015 to 2017, the phenomena frequency above the heat level in Heilongjiang Province showed an increasing trend,
while there was a significant decline in 2018. Daxinganling experienced 1 and 2 unusual cold weather (− 40 ~ − 35 ◦ C) events in 2016
and 2018, respectively.
Similarly, in 2015–2018, except for Qiqihar, Daqing, and Qitaihe (comfortable humidity zone), high humidity levels (>65%)
frequently occurred in various regions. During the study period, the areas with the highest frequency of high humidity levels were
Suihua, Heihe, and Yichun. The areas with the highest frequency of low humidity levels (<45%) were Qiqihar, Daqing, and Qitaihe.
The high humidity phenomenon in Heilongjiang Province mainly occurred in 2015 and 2016, and the low humidity phenomenon in
2018 and 2017.
The above analysis results showed that the rainfall weather in Heilongjiang Province was dominated by light rain. The wind speed
was mostly distributed between 1.51 m/s-3.3 m/s, which basically occured from October to February. In terms of temperature, Daqing,
Qiqihar and Harbin had the highest frequency of occurrence above the thermal level. Research (Wang et al., 2020a) reported on the
impact of emissions reduction on air pollution caused by reduced human activities during the COVID-19 outbreak in China stated that
low wind speeds make it difficult for air pollutants to diffuse, and high relative humidity and temperature usually accelerated the
formation of secondary PM by accelerating chemical reactions. Therefore, meteorological factors played a certain role in the gener­
ation and transmission of regional pollutants.
3.2. Temporal and spatial distribution of PM2.5 concentration
To clearly understand the temporal development of air pollutants in Heilongjiang Province, we surveyed the monthly changes in
the average PM2.5 concentration in Heilongjiang Province from February 2015 to April 2019, as shown in Fig. 3. During the study
period, the monthly provincial average PM2.5 concentration was usually less than 75 μgm− 3, and the air environment was in a clean
state (Environmental Control Quality Standards, 2012). The figure showed that the monthly distribution of PM2.5 concentration in the
province spans multiple air quality levels. The pollution stated from October to February was mild or even moderately polluted, which
may be related to household burning and agricultural activities. In addition, the dispersion conditions of pollutants in winter were
unfavorable, and vertical temperature reversals frequently occurred in winter (Cheng et al., 2016). Months with average monthly wind
Fig. 4. Monthly average change of urban PM2.5 concentration.
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speeds below 3.3 m/s in Heilongjiang Province accounted for 81.67%, and most of them occurred from October to February. As it is in
the northernmost part of China, it has a temperate continental monsoon climate and a cold-temperate climate. The winter is cold and
long. Various areas in Heilongjiang Province have been heating up since late September. When the coal-burning residential heating
system was at or close to the ground level, SO2 emissions increased by up to 24% (Hao et al., 2005). In addition, in the Northeast
agricultural region, farmers usually burn crop residues in October or November. A series of burning activities, such as coal, wood,
crops, heating, and residential cooking, increased the concentration of pollutants in the air. Guo (Guo et al., 2020) analyzed the
concentration, frequency and temporal changes of haze in China from 2013 to 2018, and concluded that the trend of haze days mainly
concentrated in winter was particularly obvious in Northeast China.
Fig. 4 showed the spatial distribution of the monthly variation (left) and daily variation (right) of the average PM2.5 concentration
in Heilongjiang Province from January 2015 to January 2019. Fig. 4 showed that the particulate matter concentration was generally
low from May to September, while the pollution was more severe from October to December. During the 1492-day period from January
1, 2015, to January 31, 2019, areas with severe pollution (> 250 μgm− 3) were Harbin (23 d), Qiqihar (2 d), Mudanjiang (2 d), Jiamusi
(6 d), Daqing (5 d), Jixi (3 d), Shuangyashan (7 d), Qitaihe (7 d), Hegang (2 d), Suihua (7 d), and generally occurred in November 2015.
In addition to the high frequency of high-temperature weather (Zheng et al., 2017), Harbin’s largest proportion of pollution days was
closely related to the energy consumption of industrial enterprises, the city’s central heating, and the rapid development of the city. In
addition, the high frequency of high humidity may be one of the reasons for the more polluted days in 2015 (Li et al., 2017d; Wang
et al., 2020b). Because of the incineration of crop residues in October or November, a series of burning activities, such as coal, wood,
crops, heating, and residential cooking, have increased the concentration of pollutants in the air.
The analysis of the frequency of occurrence of annual pollution (PM2.5 concentration > 75 μgm− 3) in cities in Heilongjiang Province
provides preliminary information for an in-depth understanding of regional pollution levels. An analysis chart was presented in Fig. 5.
The figure showed that the number of pollution days was the highest in 2015, which may be mainly affected by fires and straw burning
activities in the woody prairie and grassland of Russia’s Zabaykalsky Krai, and the prairie of Russia’s Amur Region in northeastern
China (Li et al., 2019). During the period from December 2013 to November 2018, in the study of the inter-annual variation of AOD
and PM2.5 and the frequency of fine and heavy particle pollution in all of China and five selected regions, we observed that the fine
particulate matter pollution frequency in other regions also decreased significantly every year (Hou et al., 2019), except for Northeast
China and the Pearl River Delta region. Among them, Harbin had the most polluted days, reaching 314 days, followed by the Qitai
River and Mudanjiang, and the least polluted areas were Daxinganling, Yichun, and Heihe. The cities with major air improvement
efforts were Heihe, Daxinganling, and Yichun (the energy consumption of industrial enterprises in Heihe and Daxinganling decreased
annually from 2015 to 2018; the construction area of Heihe and Daxinganling decreased annually from 2015 to 2018). This may be
Fig. 5. Frequency change of urban annual pollution (PM2.5 concentration > 75 μgm− 3).
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related to the energy consumption of industrial enterprises, urban heating, urban economic development, and the occurrence of hightemperature weather.
Comparing the PM2.5 and AOD spatial distributions in 2015 and 2017 can help us judge and effectively analyze the inter-annual
changes in pollutants. The results were shown in Fig. 6a, b, c, and d, respectively. Key information can be extracted from the figure. In
2015, the PM2.5 concentration per city was 25.5–76.67 μgm− 3, among which Harbin reached the pollution level, while Heihe, Dax­
inganling, and Yichun regions had excellent air quality environments. The air quality of the 13 cities reached the standard, which may
be attributed to the implementation rules of the Air Pollution Prevention and Control Action Plan promulgated by the Heilongjiang
Provincial Government, which played a key role.
A study by Gupta (Gupta et al., 2006) reported that satellite-derived AOD was a suitable substitute for monitoring PM air quality on
Earth. We can also observe that the regional change trend of AOD in Figures c and d was consistent with that in Figures a and b. Guo
(Guo et al., 2009) showed a good correlation (R = 0.52) in the regression analysis between MODIS AOD and daily average PM2.5. In
addition, Li (Li et al., 2017d) showed that a lower AOD had a better correlation with PM2.5. Compared with higher AOD (> 1.00), the
result of the linear fitting was that the deviation of AOD (< 0.70) was smaller, verifying the reliability and accuracy of the PM2.5
concentration data and analysis results we obtained.
3.3. Human comfort analysis
We analyzed the urban microclimate biometeorological index data closely related to air pollution and body temperature to study
how heat induction and air quality affect the comfort of the human living environment. Previous studies have shown that Tmrt (data at a
height of 1.5 m) has the highest impact on thermal comfort in severely cold winter areas (Du et al., 2020b). To obtain a clearer and
improved understanding of the radiant heat load changes between humans and the surrounding environment, this study used the
average radiant temperature to explain the relationship between human comfort and the surrounding environment. This parameter
was the basis of many human biometeorological indexes. The most important atmospheric variable was the thermal comfort for
outdoor use (Crank et al., 2020).
Table 1 summarizes the Tmrt data for the typical days from 2017 to 2020. Fig. 7 showed the histogram of the temporal and spatial
(a) 2015 mean
PM2.5 μg/m³
(b) 2017 mean
PM2.5 μg/m³
(c) 2015 mean
AOD
(d) 2017 mean
AOD
Fig. 6. Spatial distribution of PM2.5 concentration and AOD.
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Table 1
Summary of Tmrt data for typical days from 2017 to 2020.
Year
2017
City
Daqing
2018
2019
2020
2017
2018
2019
2020
Summer solstice
Autumnal equinox
Winter solstice
32.81
24.48
− 7.98
35.11
18.57
− 7.18
34.64
24.97
− 14.60
35.61
22.62
− 14.26
32.05
22.99
− 9.09
34.37
16.97
− 8.43
33.31
21.17
− 17.07
34.34
18.30
− 16.75
34.81
24.23
− 15.85
36.48
18.90
− 9.56
City
Summer solstice
Autumnal equinox
Winter solstice
Heihe
29.51
17.37
− 19.53
28.56
12.76
− 14.95
29.30
17.67
− 24.73
28.27
14.43
− 23.02
Shuangyashan
31.97
30.96
21.84
16.91
− 10.81
− 8.25
31.01
18.61
− 19.33
28.74
16.85
− 18.43
Suihua
37.16
25.02
− 19.45
35.46
19.89
− 16.83
Harbin
2017
2018
Suihua
Fig. 7. Histogram of the temporal and spatial distribution of annual average Tmrt in typical regions.
distribution of the annual average Tmrt. We can observe that the annual average Tmrt of Heilongjiang Province was distributed between
6.56 and 16.43 ◦ C, showing the characteristics of typical middle-temperate and cold-temperate regions. Among the three typical days,
the average daily Tmrt values of the five cities exhibited similar annual trends. Among them, the summer solstice and autumnal equinox
remained relatively stable, but the data value of the autumnal equinox fluctuates relatively strongly, and the fluctuation strength was
approximately 5 ◦ C. However, the winter solstice showed a clear downward trend, and the lowest value of Tmrt occurred in Heihe,
which was attributed to its geographical location. In addition, except for Suihua City, Tmrt has been decreasing annually since 2017,
among which Daqing City and Harbin City have relatively high values. From the changes in Tmrt development trends in various regions,
it can be preliminarily inferred that this may be related to the comprehensive energy consumption of industrial enterprises, con­
struction area, total urban central heating, GDP, urban transportation and other social development factors, temperature, geographical
location, etc.
3.4. Analysis of social development factors
Several factors affected human exposure to air pollution. In addition to meteorological parameters and PM2.5, social development
factors, such as ECIE, PDUA, FSBU, AUH, PCB, HSHW, and GDP, also played an important role in urban development. Related studies
found a close relationship between socioeconomic factors and PM2.5 (Wu et al., 2020; Yang et al., 2020). For this reason, we collected
data on social development factors, as shown in Table 2, and analyzed the gradual changes in social and economic indicators, as shown
in Fig. 8.
From the table, we can read the key information: As far as ECIE was concerned, the downward trend in various regions was slow,
and the ECIE of Daqing far exceeds that of other cities, followed by Harbin. The PDUA of various regions remained stable within four
years, however, Jiamusi showed a clear downward trend. It is worth pointing out that the PDUA of Harbin and Jixi was relatively large.
The results of FSBU showed that Harbin’s FSBU ranked first in the province, but the construction volume had dropped significantly in
recent years, while the improvement efforts in other cities have been significantly weaker. In AUH, Daqing ranked in the top position
with a faster growth rate, followed by Harbin. Harbin, Daqing, and Qiqihar were firmly in the top three in PCB, HSHW, and GDP.
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Table 2
Summary of social development factors.
Factors
Year
Harbin
Qiqihar
Jixi
Hegang
Shuang yashan
Daqing
ECIE (×104 tons of SCE)
2014
2015
2016
2017
2014
2015
2016
2017
2014
2015
2016
2017
2014
2015
2016
2017
2014
2015
2016
2017
2014
2015
2016
2017
2014
2015
2016
2017
71.38
71.53
71.56
60.98
104.18
113.66
104.16
106.22
58.39
59.63
54.57
47.34
19.00
21.00
24.00
26.00
1278.77
1342.27
1342.72
1362.46
139.80
143.09
144.65
155.40
338.67
421.17
447.27
471.28
50.37
46.99
38.47
39.03
77.99
77.99
76.98
77.69
11.59
11.36
10.36
10.13
11.00
12.00
22.00
24.00
0.89
0.81
43.65
43.65
24.94
25.15
27.27
18.81
57.31
59.70
62.08
63.67
30.56
28.23
24.59
25.06
91.37
89.68
89.52
85.80
4.69
6.43
6.54
6.07
12.00
0.00
11.00
8.00
96.90
93.59
93.58
100.38
8.85
8.97
9.37
10.30
16.41
16.26
16.48
16.55
19.90
19.06
27.21
29.01
65.06
64.98
64.71
64.12
2.13
1.69
1.48
1.28
4.00
3.00
10.00
12.00
98.00
96.26
91.73
85.37
12.16
12.44
13.13
17.26
13.02
13.13
13.16
14.15
39.28
37.23
40.22
37.70
39.75
40.51
40.51
40.68
3.03
2.53
2.68
2.65
3.00
2.00
7.00
0.00
52.32
47.53
48.41
49.50
7.00
7.00
5.42
9.85
12.78
11.75
12.14
12.14
174.95
159.36
165.81
171.44
47.61
46.67
45.42
43.11
18.31
17.50
11.07
8.47
19.00
26.00
29.00
35.00
200.89
201.90
165.72
149.39
59.98
63.07
67.75
63.95
346.53
244.76
210.08
229.07
2014
2015
2016
2017
2014
2015
2016
2017
2014
2015
2016
2017
2014
2015
2016
2017
2014
2015
2016
2017
2014
2015
2016
2017
2014
2015
2016
2017
Yichun
13.42
12.47
16.19
21.75
43.83
41.96
41.95
42.55
1.26
1.35
1.19
0.72
3.00
7.00
8.00
15.00
46.10
50.00
35.25
37.81
11.85
12.91
13.66
14.24
15.90
14.75
15.41
16.50
Jiamusi
15.31
14.41
13.87
14.62
62.28
61.87
61.77
31.81
7.21
6.18
5.30
4.57
7.00
14.00
1.00
0.00
96.13
90.00
95.44
95.62
13.30
13.50
12.70
13.10
38.70
40.63
43.14
45.62
Qitaihe
44.28
42.73
41.19
41.64
13.14
59.85
21.40
60.71
1.47
1.41
1.18
1.04
18.00
0.00
21.00
29.00
70.00
70.00
75.00
89.43
9.45
9.50
9.71
8.88
15.77
16.01
16.39
17.58
Mudan jiang
20.32
19.11
18.95
17.21
77.68
79.52
79.52
72.83
14.34
17.48
17.14
14.95
10.00
13.00
15.00
20.00
147.07
205.93
149.35
140.27
17.76
18.45
20.07
21.11
31.86
32.61
34.79
35.89
Heihe
8.76
9.03
8.11
7.84
51.79
52.15
52.15
53.12
2.46
3.02
2.43
1.73
11.00
12.00
16.00
21.00
11.25
11.00
10.20
10.40
6.20
6.20
6.76
6.76
2.90
3.02
3.37
3.50
Suihua
19.11
21.70
23.47
23.98
37.84
38.27
39.56
39.56
6.92
9.55
6.65
6.27
6.00
6.00
6.00
7.00
27.62
27.65
5.60
0.00
1.83
6.44
6.44
12.41
14.32
15.48
16.67
17.66
PDUA (100 persons/sq.km)
FSBU (×105 sq.m)
AUH (unit)
PCB (×105 person-times)
HSHW (×105 gigajoules)
GDP (×108 yuan)
ECIE (×104 tons of SCE)
PDUA (100 persons/sq.km)
FSBU (×105 sq.m)
AUH (unit)
PCB (×105 person-times)
HSHW (×105 gigajoules)
GDP (×108 yuan)
Surprisingly, Harbin far surpassed other regions and has an upward trend. This information fully explained the cause of severe air
pollution in Harbin.
Fig. 8 illustrated the GDP growth rate of each city (left picture) and the annual GDP distribution in Heilongjiang Province (right
picture). The economic output values of Harbin and Daqing were significantly better than those of other cities, followed by Qiqihar and
Mudanjiang. Suihua, Harbin, Jiamusi, and Mudanjiang showed sustained and steady development.
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Urban Climate 41 (2022) 101070
J. Liu et al.
Fig. 8. GDP growth rate of each city (left picture) and the distribution of annual GDP in Heilongjiang Province (right picture) from 2014 to 2017.
4. Discussion
4.1. Correlation between variables
To explore the correlation between variables, Li (Li et al., 2018b) used a correlation coefficient to evaluate the relationship between
Fig. 9. Matrix diagram of correlation coefficients for PM2.5 concentration, meteorological parameters, and social development factors. (Note:
Effective value* = p < 0.05, ** = p < 0.01).
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Urban Climate 41 (2022) 101070
J. Liu et al.
Air Quality Index (AQI) and PM2.5. Similarly, He (He et al., 2016) determined the linear relationship between AOD and influencing
factors, including socioeconomic indicators. Xu (Xu et al., 2020b) found a linear relationship between PM2.5 concentration and
meteorological conditions. In this study, taking factors as the entry point to improve air quality and human comfort, and maintaining
stable social and economic development, we used the Spearman correlation coefficient to analyze the correlation strength among
various factors, including PM2.5 concentration, meteorological parameters, human comfort, and social development factors, to better
understand the relationship among them. The null hypothesis of the correlation test is the correlation between any two variables. The
p-value shown in Fig. 9 indicated the possibility of determining the zero-correlation coefficient. When this probability was below 5%,
the correlation coefficient was statistically significant (p < 0.05).
The Spearman correlation coefficient matrix between the variables is shown in Fig. 9. The sample points are the annual average
data for the 12 cities from 2015 to 2017. The Sperman correlation coefficient between the two variables was determined by analyzing
the collected 12 variables. PM2.5 concentration was weakly correlated with ECIE and T (+). The relationship among between GDP and
ECIE, FSBU, PCB, AUH, and HSHW was moderate (+), and there was a weak (+) and strong correlation (+) with PDUA and Tmrt,
respectively. Research on the correlation of Tmrt showed that it has a strong relationship with ECIE, FSBU, HSHW, GDP, and T (+). It
can be observed that urban air pollution was mainly caused by the combined effect of human activities and meteorological parameters.
This finding also verified the above analysis results of PM2.5 concentration and Tmrt data in Heilongjiang Province. This section pro­
vided an analysis basis for determining the best fit and sensitivity factors.
4.2. Sensitivity factor determination
This work used regression analysis methods to find sensitivity factors for PM2.5, socioeconomic indicators, and human comfort
indicators. For this purpose, we performed a stepwise regression analysis on the 10 factors (including ECIE, PDUA, FSBU, AUH, PCB,
HSHW, Rf, Ws, T, and Rh) and GDP, PM, and Tmrt of the five cities mentioned above. Table 3 presented the results of the analysis. After
the model is automatically identified, the remaining two factors, ECIE and the quantity of heat supplied by hot water, are combined
with regression coefficients to determine that the significant sensitivity factor of GDP is HSHW. Similarly, the significant sensitivity
factor of PM2.5 concentration was PCB. In the stepwise regression analysis of the data set with Tmrt as the dependent variable, the
system automatically eliminated independent variables that were relatively insignificant and caused multicollinearity, with only, ECIE
remaining. Although the model failed the F test, the Spearman correlation coefficient matrix in the previous section confirmed that
ECIE and Tmrt were significantly positively correlated.
Further, we considered the comprehensive impact of various social factors and meteorological parameters on GDP, PM, and Tmrt,
and explored the sensitivity factors that affected the three indicators. The entropy method is a mathematical method used to judge the
dispersion degree of a certain index. The higher the dispersion degree, the higher the impact of this indicator on the comprehensive
evaluation. As a comprehensive evaluation method, the entropy method has been widely used in all aspects (Gong et al., 2020; Zhao
et al., 2020; Tian et al., 2020). We recorded the comprehensive score obtained after preprocessing and entropy analysis of the threeindicator data as INDEX, representing the development of the three indicators for each sample. To identify sensitive factors, the
Spearman’s correlation and stepwise regression were used to analyze the relationship between the above-mentioned 10 factors and
INDEX. The correlation results showed that INDEX was significantly correlated with FSBU, AUH, and HSHW (+), which reflected the
significant impact of social activities on the economy, air quality, and human comfort. Regression model screening and test results
revealed that AUH was the most important contributor to INDEX. The regression model results are presented in Table 4. In order to
evaluate the robustness of the model performance, we adopted the leave-one-out cross-validation (LOOCV) to evaluate the accuracy of
each regression model. The LOOCV method is to randomly divide the data set into k (number of samples) classes, using one as the test
set each time, and use the remaining k-1 classes as the training set. Alternation is calculated k times as the test set. Find the root mean
square error (RMSE) every time, and sum it up and average it. The RMSE of the three models, as an accuracy evaluation index, are
summarized in Tables 3 and 4, respectively. The evaluation indicators, R2 and RMSE, all showed that the model fit was reasonable. This
study explained the reasons behind the changes in Heilongjiang’s economy, environmental quality, and human comfort. On the
premise of expanding the research sample size, using this method for long-term research can be useful in the future to provide humans
with a high-quality life.
5. Conclusions
Human activities and natural factors affect urban air quality. However, this type of research mainly focuses on single-factor analysis
of first- and second-tier cities in warm temperate and subtropical regions. In order to effectively improve the level of environmental
governance in severe cold regions, this work explores the temporal and spatial distribution of PM2.5 concentration and its influencing
factors in Northeast China. On this basis, the research uses correlation coefficient, entropy method and stepwise regression analysis
method to carry out quantitative analysis from the perspective of social development and meteorology to achieve the goal, which is to
reduce the harm caused by air pollutants to human health and the environment while ensuring the stable development of society and
economy. The major findings are summarized as follows:
(1) The pollution period was from October to February, and the number of days when pollution occurred in 2015 was the highest. In
addition, the air quality of various cities reached severe levels in November 2015. Harbin had the longest annual and severe
pollution periods, reaching 314 days and 23 days, respectively. In Daxinganling, Yichun, and Heihe, the probability of their
occurrence was the lowest.
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J. Liu et al.
Table 3
Summary of stepwise regression analysis results under a single indicator.
Independent variable:Gross Domestic Product (*P < 0.05, **P < 0.01)
Independent variable
Regression coefficients
t
VIF
Constant
Energy Consumption of Industry Enterprise
Quantity of heat supplied hot water
0.000
0.098
0.949
0.000
4.733**
45.764**
_
1.261
1.261
AIC
R2
Adjusted R2
F value
RMSE
423.813
0.996
0.994
1459.431(p = 0.000)
0.044
Independent variable:PM2.5 (*P < 0.05, **P < 0.01)
Independent variable
Regression coefficients
t
VIF
Constant
Number of passengers carried by bus, Trolley Bus
0.000
0.563
0.000
2.458*
_
1.000
AIC
R2
Adjusted R2
F value
RMSE
79.179
0.317
0.265
6.041(p = 0.029)
0.272
Table 4
Stepwise regression analysis results under INDEX.
Independent variable:INDEX (*P < 0.05, **P < 0.01).
Independent variable
Regression coefficients
t
VIF
Constant
Number of automobile per 100 urban households
0.369
0.206
29.673*
23.154*
_
1.000
AIC
R2
Adjusted R2
F value
RMSE
− 22.375
0.998
0.996
536.093(p = 0.027)
0.017
(2) The PM2.5 concentration of cities in Heilongjiang Province was weakly correlated with ECIE and T (+). The relationship among
between GDP and ECIE, FSBU, PCB, AUH, and HSHW was moderate (+), and there was a weak correlation (+) and strong
correlation (+) with PDUA and Tmrt, respectively. There was a strong connection among between Tmrt and ECIE, FSBU, HSHW,
GDP, and T (+). The above results collectively indicated that the social pattern was closely related to the air quality of the city,
and it is recommended to achieve higher air quality and fewer pollutant emissions through social pattern planning and man­
agement policies. These policies should focus on reducing the overall energy consumption of industrial enterprises and the
amount of construction work.
(3) The most significant contributor to the PM2.5 concentration of five cities including Daqing, Harbin, Shuangyashan, Heihe and
Suihua was PCB. In addition, the biggest contributor of INDEX was AUH. This reflected that AUH can be controlled to achieve
the goal of reducing PM2.5 concentration on the premise of ensuring stable social and economic development and reducing air
pollutants to human health.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant nos. 52041601). The authors are especially
grateful to the editors and referees who gave important comments that helped us improve this paper.
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J. Liu et al.
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