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Citation: Guo, R.; Qi, Y.; Zhao, B.; Pei,
Z.; Wen, F.; Wu, S.; Zhang, Q.
High-Resolution Urban Air Quality
Mapping for Multiple Pollutants
Based on Dense Monitoring Data and
Machine Learning. Int. J. Environ. Res.
Public Health 2022,19, 8005. https://
doi.org/10.3390/ijerph19138005
Academic Editors: Gianluigi de
Gennaro, Jolanda Palmisani and
Alessia Di Gilio
Received: 26 April 2022
Accepted: 28 June 2022
Published: 29 June 2022
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International Journal of
Environmental Research
and Public Health
Article
High-Resolution Urban Air Quality Mapping for Multiple
Pollutants Based on Dense Monitoring Data and
Machine Learning
Rong Guo 1, Ying Qi 1, Bu Zhao 2, Ziyu Pei 1, Fei Wen 3, Shun Wu 4and Qiang Zhang 1,*
1Department of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China;
louisguo626@gmail.com (R.G.); qiying@nwnu.edu.cn (Y.Q.); yuzipe@163.com (Z.P.)
2School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA;
zhaobu@umich.edu
3Gansu Academy of Eco-Environmental Sciences, Lanzhou 730070, China; wenfei258@126.com
4Sichuan Meteorological Service Centre, Chengdu 610072, China; wushunws6@163.com
*Correspondence: zhangq@nwnu.edu.cn; Tel.: +86-0931-7971928
Abstract:
Spatially explicit urban air quality information is important for urban fine-management
and public life. However, existing air quality measurement methods still have some limitations
on spatial coverage and system stability. A micro station is an emerging monitoring system with
multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we
proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By
using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed
a decision tree model to infer the distribution of citywide air quality at a 500 m
×
500 m
×
1 h
resolution, with a coefficient of determination (R
2
) value of 0.740 for PM
2.5
, 0.754 for CO and 0.716
for SO
2
. Meanwhile, we also show that the deployment density of the monitoring stations can have a
significant impact on the air quality inference results. Our method is able to show both short-term and
long-term distribution of multiple important pollutants in the city, which demonstrates the potential
and feasibility of dense monitoring data combined with advanced data science methods to support
urban atmospheric environment fine-management, policy making, and public health studies.
Keywords:
air quality mapping; high-resolution; micro monitoring stations; LCS network;
machine learning
1. Introduction
Urban air pollution seriously affects public health in both developed and developing
countries [
1
–
3
]. It is worth noting that more than 50% of the world’s population lives in
urban areas where most of the air pollution-related health impact occurs [4].
Because of the severe urban air pollution, there is a growing demand for air quality
monitoring services, which aim to understand the real-time air quality of any area in
the city (e.g., street, community, school), as well as their spatial–temporal variations at a
higher resolution [
5
]. Such information could greatly help public decision making (such
as whether to take exercise outdoors) and urban fine-management and policy making
(such as performing controls for the area that often produce severe air pollution) to reduce
public health and capital loss. However, inferring the citywide air quality at both a high
spatial and temporal resolution can be extremely challenging. First, the standard air quality
monitoring stations are limited worldwide due to their high cost and space limitation.
Even in affluent regions, air quality monitors are also sparse (i.e., there are 18 continuous
regulatory monitors in the New York metropolitan area [
6
] and 35 in Beijing, China [
7
]),
which provide only limited coverage of air quality monitoring. Second, the air pollutants’
distribution can vary greatly at both small spatial and temporal scales due to complex
Int. J. Environ. Res. Public Health 2022,19, 8005. https://doi.org/10.3390/ijerph19138005 https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022,19, 8005 2 of 15
physical–chemical transformations and multiple emission sources, especially in populous
urban areas [
5
,
8
]. Third, urban air pollution is a complex dynamic system, which not only
has a strong spatial dependency with adjacent areas [
9
], but also can be affected by a variety
of factors (e.g., meteorological, urban structures).
Currently, a variety of methods have been used to infer urban air quality, but still with
various limitations. The classical dispersion models and chemical transport models are
in most cases a function of meteorology, receptor locations, traffic volumes, and emission
factors. These models can offer high fidelity but tend to be computationally expensive
and need empirical assumptions and parameters [
10
–
12
]. Spatial interpolation methods
are based on the air quality reports from nearby monitoring stations, which are usually
employed by public websites releasing AQIs. However, their inference accuracy is often
not guaranteed due to air quality varying across locations non-linearly [
13
]. Land-use
regression can provide air quality estimations with a high spatial resolution but lack
sufficient temporal resolution. Meanwhile, it highly relies on the availability of updated
local land-use data [
14
–
16
]. Some studies use satellite remote sensing data to estimate air
quality [
17
,
18
], but they are usually spatially coarse (1
−
10 km resolution) [
19
] and easily
affected by cloudy weather and water/snow glint reflectance [20,21].
In recent years, many studies have utilized machine learning methods and low-cost
sensor (LCS) technology for urban air quality inference modeling, and it has been proved
to have a better performance [
5
,
22
]. However, we note that existing studies still needs
further improvement. First, most studies based on such as land-use regression or satellite
remote sensing lack a sufficient temporal resolution, which can only model daily variations
in urban air quality [
19
,
23
,
24
]. In turn, urban air pollution showed obvious differences
over continuous hours [
5
,
8
]; thus, it is necessary to understand the hourly variations in air
quality for public daily decision-making and urban fine-management. Second, regarding
spatial resolution, many studies inferred air quality at coarse resolutions (10 km) [
23
,
25
–
27
],
with only a few exceptions at 1 km [
28
,
29
]. However, in multicenter health studies, there
are dense residents in many urban areas where air pollution exposures may vary greatly
within a 10-km
2
or smaller grid cells. Third, the LCS sensors have higher spatial–temporal
measurement resolution [
5
,
30
], but existing LCS-based methods still have some limitations.
The portable monitoring devices are not necessarily accurate due to cost and volume
limitations, and often focus on a specific area rather than the whole city [14]. The vehicles
equipped with sensors cannot guarantee the monitoring time (less observation at night)
and are easily affected by human factors (forgetting to open) or operating environments
(the wind in driving) [
19
,
31
]. Therefore, it is necessary to explore a method to steadily infer
the variations in urban air quality at both a high spatial and temporal resolution.
In this paper, we propose a method to address the challenges of high-resolution urban
air quality mapping by exploring the potential of combining dense LCS networks and
machine learning techniques. Specifically, we collected air quality data from 448 micro
monitoring stations (micro station) in Lanzhou City. Then, we developed a decision tree
model to infer the citywide air quality at a 500 m
×
500 m
×
1 h resolution by using these
dense monitoring data and external data (meteorological and land-use data).
The results
show that our method can accurately infer the distribution of and variation in multiple
important pollutants at ahigh spatio-temporal resolution, which demonstrates the potential
and feasibility of dense monitoring data for urban air quality mapping, and provide
support for urban atmospheric environment fine-management, policy making, and public
health studies.
2. Materials and Methods
2.1. Study Area and Micro Stations Distribution
Lanzhou is the capital of Gansu Province, China, which covers about 1073 km
2
with
four administrative districts. The Xigu District is the core industrial area, Anning District
is the science and education center, and Qilihe District and Chengguan District are the
business areas. As a center for the petrochemical industry and heavy industry and also
Int. J. Environ. Res. Public Health 2022,19, 8005 3 of 15
an important transportation hub, it suffers from severe air pollution. Therefore, a dense
air quality monitoring network with 448 micro stations (about 0.4 stations per square
kilometer) has been established here to assist in air pollution control. Most of these stations
are located in the core area with a high density (335 stations in 184 km
2
, about 2 stations
per square kilometer) (Figure 1). The dense network can monitor the hourly concentrations
of both particulate matters (PM
2.5
, PM
10
) and gaseous pollutants (CO, O
3
, NO
2
, and SO
2
)
(Supplementary Material Table S1). Figure S1 shows the station distribution number of the
micro stations in each administrative district.
Int. J. Environ. Res. Public Health 2022, 19, 8005 3 of 15
2. Materials and Methods
2.1. Study Area and Micro Stations Distribution
Lanzhou is the capital of Gansu Province, China, which covers about 1073 km
2
with
four administrative districts. The Xigu District is the core industrial area, Anning District
is the science and education center, and Qilihe District and Chengguan District are the
business areas. As a center for the petrochemical industry and heavy industry and also an
important transportation hub, it suffers from severe air pollution. Therefore, a dense air
quality monitoring network with 448 micro stations (about 0.4 stations per square
kilometer) has been established here to assist in air pollution control. Most of these
stations are located in the core area with a high density (335 stations in 184 km
2
, about 2
stations per square kilometer) (Figure 1). The dense network can monitor the hourly
concentrations of both particulate matters (PM
2.5
, PM
10
) and gaseous pollutants (CO, O
3
,
NO
2
, and SO
2
) (Supplementary Material Table S1). Figure S1 shows the station
distribution number of the micro stations in each administrative district.
Figure 1. Study area and the distribution of micro stations in Lanzhou City, China, where the red
dots represent the stations in the core area.
2.2. Data Collection
2.2.1. Air Quality Data
In this study, we focused on urban air quality in winter, when severe air pollution is
more likely to occur and vary greatly (Figure S2). A one-phase air quality
data collection
campaign was conducted from 11 October 2021 to 19 February 2022 in Lanzhou City. The
air
quality data comes from 448 micro stations, including real-time air pollutant
concentrations, collection timestamp, and collection location (longitude, latitude, and
street). A total of 1,339,055 h of air quality
monitoring data were obtained after removing
erroneous data, and the data-missing rate was 2.3% in the time series. Figure S2 shows the
regional average of the air pollutant concentrations during the study period. All micro
stations are managed by the Department of Ecology and Environment of Gansu Province,
China. They are factory calibrated and regularly maintained to ensure the readings have
acceptable precision. More details about the parameters and performance of the
monitoring equipment are presented in the Supplementary Material.
Figure 1.
Study area and the distribution of micro stations in Lanzhou City, China, where the red
dots represent the stations in the core area.
2.2. Data Collection
2.2.1. Air Quality Data
In this study, we focused on urban air quality in winter, when severe air pollution is
more likely to occur and vary greatly (Figure S2). A one-phase air quality data collection
campaign was conducted from 11 October 2021 to 19 February 2022 in Lanzhou City. The air
quality data comes from 448 micro stations, including real-time air pollutant concentrations,
collection timestamp, and collection location (longitude, latitude, and street). A total of
1,339,055 h of air quality monitoring data were obtained after removing erroneous data,
and the data-missing rate was 2.3% in the time series. Figure S2 shows the regional average
of the air pollutant concentrations during the study period. All micro stations are managed
by the Department of Ecology and Environment of Gansu Province, China. They are factory
calibrated and regularly maintained to ensure the readings have acceptable precision. More
details about the parameters and performance of the monitoring equipment are presented
in the Supplementary Material.
2.2.2. Meteorological and Land-Use Data
We also collected meteorological conditions data during the study period. The me-
teorological data comes from 51 streets in Lanzhou city, including weather, temperature,
relative humidity, wind direction, wind level, collection timestamp, and collection location
(longitude, latitude, and street). Finally, a total of 118,773 h of meteorological data were
Int. J. Environ. Res. Public Health 2022,19, 8005 4 of 15
obtained. Our study uses the land-use data of Lanzhou city at a 30 m spatial resolution;
there are 6 first-level types, including cultivated land, forest land, grassland, water area,
construction land, unused land, and 25 s-level types, including forest land, shrubland,
sparse forest land, other forest land, and high, medium, and low coverage grassland.
2.3. City Grid
First, the geographical coordinate system WGS2000 was used for the geographical
registration of the entire study area, and the city vector shape was extracted. We divided
the study area into 4139 grids based on the distribution density of the micro stations, and
each grid size was 500 m
×
500 m. Next, all micro stations, as well as the air quality
monitoring data, were assigned to the corresponding grids based on their location (latitude
and longitude). The pollutant concentrations of each grid area were from the observed
value of its corresponding micro station. For grids that contain multiple stations, we
took the average value of the multiple stations and assigned it to these grids. Finally,
for all grids in the study area, including the blank grids without assigned micro stations,
the spatially adjacent data were searched and allocated to the grids based on the spatial
distance between each grid and adjacent units. We obtained high-resolution data for all
grids in the study area: (1) Location: longitude and latitude; (2) Air quality data: air quality
monitoring data from the nearest 10 micro stations at the same hour (i.e., spatial neighbors);
(3) Meteorological data: hourly weather, temperature, relative humidity, wind direction,
and wind force data from the nearest street; (4) Land-use data: the size of each of the
25 land-use
types in each grid. These data were used in the next step as the input variables
of each grid for developing the air quality inference model for the entire study area.
2.4. Air Quality Inference Model
2.4.1. Model Construction
For urban air quality, the air pollutant concentrations of a given grid is spatially
correlated to its adjacent units. For example, the air quality of a location is likely to
be bad if the air quality of its adjacent areas is bad. In addition, external factors, such
as meteorological conditions, can affect regional pollutant transport and local pollutant
deposition. In order to capture the potential impact of multiple factors on urban air
quality, we developed a machine learning model based on Extreme Gradient Boosting
(XGBoost) [
32
] to infer the air quality of a given grid by using multiple spatially adjacent
data related to the grid.
The XGBoost algorithm was improved based on the gradient boosted decision
tree (GBDT), which offered additional functions (e.g., column sampling and shrinkage)
to avoid overfitting and enhance the model predictability [
32
]. By introducing the
regularization item to measure the complexity of the tree model in the objective function,
XGBoost can reduce the risk of model overfitting. The decision tree was used as the basic
learner of XGBoost, and the weight of the learner was updated by the error gained from
each iteration. Finally, the learners with different weights formed an ensemble model,
and the prediction was generated by the weighted average. In general, XGBoost has
better accuracy due to its additional training process, and takes less time to build the
model. In our study, the nonlinear spatial correlation of air pollution in adjacent areas,
heterogeneous external data, and the advantages of XGBoost being fast and accurate
were integrated into a universal study framework for urban air quality inference. As
shown in Figure 2, our goal is that the framework can derive hourly citywide air quality
inference as soon as updated data are available.
Int. J. Environ. Res. Public Health 2022,19, 8005 5 of 15
Int. J. Environ. Res. Public Health 2022, 19, 8005 5 of 15
Figure 2, our goal is that the framework can derive hourly citywide air quality inference
as soon as updated data are available.
Figure 2. Study framework.
2.4.2. Variable Selection and Model Hyperparameters
A variety of methods are used to further construct the inference model. We selected
the needed predictors by the feature importance of the XGBoost algorithm, which is a
backward elimination procedure (i.e., gradually removing the variables with the lowest
importance). The model hyperparameters are optimized by a parameter search method
called GridSearchCV with 5-fold cross-validation, choosing the hyperparameters set that
minimizes RMSE. The search range of different hyperparameters is shown in Table 1. The
experiments were mainly run on a computing server, and the model was built using
python (hardware information and software versions are shown in Table S2).
Table 1. Hyperparameters search range in the GridSearchCV.
Hyperparameter
Range
Interval
n_estimators
100~500
100
learning_rate
0.05~0.1
0.01
max_depth
3~10
1
min_child_weight
1~6
1
colsample_bytree
0.7~1
0.1
Subsample
0.7~1
0.1
2.4.3. Evaluation Metric
In our study, three metrics were used to evaluate the air quality infer performance,
where is the true value, is the inference value, and is the sample mean.
Root mean squared error (RMSE):
RMSE =(
)
(1)
Coefficient of determination (R2):
R= 1 ()
()
(2)
Pearson correlation coefficient (COR):
Figure 2. Study framework.
2.4.2. Variable Selection and Model Hyperparameters
A variety of methods are used to further construct the inference model. We selected
the needed predictors by the feature importance of the XGBoost algorithm, which is a
backward elimination procedure (i.e., gradually removing the variables with the lowest
importance). The model hyperparameters are optimized by a parameter search method
called GridSearchCV with 5-fold cross-validation, choosing the hyperparameters set that
minimizes RMSE. The search range of different hyperparameters is shown in Table 1.
The experiments
were mainly run on a computing server, and the model was built using
python (hardware information and software versions are shown in Table S2).
Table 1. Hyperparameters search range in the GridSearchCV.
Hyperparameter Range Interval
n_estimators 100~500 100
learning_rate 0.05~0.1 0.01
max_depth 3~10 1
min_child_weight 1~6 1
colsample_bytree 0.7~1 0.1
Subsample 0.7~1 0.1
2.4.3. Evaluation Metric
In our study, three metrics were used to evaluate the air quality infer performance,
where yiis the true value, ˆ
yiis the inference value, and yiis the sample mean.
Root mean squared error (RMSE):
RMSE =s∑n
i=1(yi−ˆ
yi)2
n(1)
Coefficient of determination (R2):
R2=1−∑n
i=1(yi−ˆ
yi)2
∑n
i=1(yi−y)2(2)
Pearson correlation coefficient (COR):
COR =COV(ˆ
yi,yi)
pVar[ˆ
yi]Var[yi](3)
Int. J. Environ. Res. Public Health 2022,19, 8005 6 of 15
3. Results
3.1. Data Analysis
3.1.1. Spatio-Temporal Characteristics for Urban Air Pollutants
Taking PM
2.5
as an example, the highest level of concentration appeared on 15 February
2022, 9:00 p.m. (129.3 micrograms per cubic meter (
µ
g/m
3
)) and the lowest level appeared
on 12 October 2021, 8:00 a.m. (17.9
µ
g/m
3
). For spatial distribution, the highest level of
the PM
2.5
concentration average was observed at a thermal power plant in Xigu District
(
68.2 µg/m3
) and the lower level was obtained at a village in the suburbs of Chengguan
District (42.7
µ
g/m
3
). Figure 3a shows the PM
2.5
concentration differences between two
adjacent stations at the same hour during the study period, with about 24.3% of the data
having differences higher than 10 micrograms per cubic meter (
µ
g/m
3
). Similarly, with
about 14.5% of the data having differences over 10
µ
g/m
3
for the same stations between two
consecutive hours. Several studies have shown that a 10
µ
g/m
3
increase in PM
2.5
pollution
can significantly increase the all-cause mortality as well as respiratory and cardiovascular
disease hospitalizations [
33
–
36
]. These results show that urban PM
2.5
pollution has a
relatively significant variation at both small temporal and spatial scales. This may be
the result of a complex urban structure, human activities, and the uneven distribution of
various emission sources. In addition, it also shows the effectiveness of micro stations to
capture the significant spatial heterogeneity of urban air pollutants.
Int. J. Environ. Res. Public Health 2022, 19, 8005 6 of 15
COR 𝐶𝑂𝑉𝑦
, 𝑦
𝑉𝑎𝑟𝑦
𝑉𝑎𝑟𝑦
(3)
3. Results
3.1. Data Analysis
3.1.1. Spatio-Temporal Characteristics for Urban Air Pollutants
Taking PM
2.5
as an example, the highest level of concentration appeared on February
15, 2022, 9:00 p.m. (129.3 micrograms per cubic meter (μg/m
3
)) and the lowest level
appeared on October 12, 2021, 8:00 a.m. (17.9 μg/m
3
). For spatial distribution, the highest
level of the PM
2.5
concentration average was observed at a thermal power plant in Xigu
District (68.2 μg/m
3
) and the lower level was obtained at a village in the suburbs of
Chengguan District (42.7 μg/m
3
). Figure 3a shows the PM
2.5
concentration differences
between two adjacent stations at the same hour during the study period, with about 24.3%
of the data having differences higher than 10 micrograms per cubic meter (μg/m
3
).
Similarly, with about 14.5% of the data having differences over 10 μg/m
3
for the same
stations between two consecutive hours. Several studies have shown that a 10 μg/m
3
increase in PM
2.5
pollution can significantly increase the all-cause mortality as well as
respiratory and cardiovascular disease hospitalizations [33–36]. These results show that
urban PM
2.5
pollution has a relatively significant variation at both small temporal and
spatial scales. This may be the result of a complex urban structure, human activities, and
the uneven distribution of various emission sources. In addition, it also shows the
effectiveness of micro stations to capture the significant spatial heterogeneity of urban air
pollutants.
Figure 3. (a) PM
2.5
hourly concentration differences between two adjacent stations at the same hour;
(b) PM
2.5
hourly concentration differences for the same stations between two consecutive hours; (c)
Spearman correlation between PM
2.5
pollution and the predictors; (d) feature importance of different
predictors for PM
2.5
pollution inference.
Figure 3.
(
a
) PM
2.5
hourly concentration differences between two adjacent stations at the same
hour; (
b
) PM
2.5
hourly concentration differences for the same stations between two consecutive
hours;
(c) Spearman
correlation between PM
2.5
pollution and the predictors; (
d
) feature importance
of different predictors for PM2.5 pollution inference.
Int. J. Environ. Res. Public Health 2022,19, 8005 7 of 15
3.1.2. Correlation and Feature Importance
First, Spearman correlation analysis was conducted on PM
2.5
pollution and Figure 3c
shows the correlation between the PM
2.5
data and other spatially adjacent data. It can
be seen that the closer stations have more significant impacts on local PM
2.5
pollution,
which follows the First Law of Geography [
37
]; i.e., “Everything is related to everything else,
but near things are more related than distant things”. Meanwhile, the external data are also
correlated with PM
2.5
pollution. Then, we used the XGBoost algorithm to calculate the
feature importance of different predictors for PM
2.5
pollution inference. The result is shown
in Figure 3d; overall, the pollution data from the nearest two micro stations have the highest
importance for PM
2.5
inference performance, which is consistent with the result in the
Spearman correlation analysis. In meteorological conditions, both relative humidity and
temperature are of high importance to PM
2.5
inference. For the impact of temperature on
model inference, this may be due to the winter heating activities that vary with temperature.
This phenomenon is consistent with Mateusz Zar˛eba and Tomasz Danek’s study in Krakow,
Poland [
38
]. They proved that temperature has a direct and important impact on the PM
concentration in winter/early spring months. Relative humidity also shows obvious impact
on PM
2.5
inference, which may be related to its fluctuation [
39
]. Significantly, the wind
direction had the lowest F-score for PM
2.5
inference, which may be closely related to the
unobvious wind direction variations in Lanzhou city. Among the meteorological data
collected, the northeasterly wind accounts for about 63.6%, and the non-sustained wind
accounts for about 19.7%. Similarly, for wind force, about 36% of the data were of the same
wind force, which may be the reason for its relatively low impact on PM2.5 inferences.
3.2. Performance on Air Quality Inference
One common challenge faced by urban air quality inference is the lack of sufficient
monitors to provide a spatially distributed benchmark as the ground truth. In our study
area, there are 448 micro stations located in various regions, which enable us to develop
and validate the air quality inference model based on their monitoring data. Specifically,
we first used the data from grids assigned with micro stations for ground truthing, to train
and test the air quality inference model. Then, the tested model was used to estimate the
air quality of all grids in the study area.
We developed and tested various methods for making air quality inference.
Support Vector Regression (SVR)
: SVR is expected to find an optimal fitting line
so that all data points can be as close as possible to this line, thus making a prediction.
The SVR model in our study was developed based on the “sklearn” package in Python.
k-Nearest Neighbor (KNN)
: The core of the KNN algorithm is to find knearest
neighbors of a given sample in the feature space and assign the attributes’ average of
these neighbors to the sample for prediction. KNN and SVR are both classical regression
algorithms, which are taken as the baselines to compare the performance improvement of
existing popular algorithms in air quality inference tasks. The KNN model in our study
was developed based on the “sklearn” package in Python, and the number of neighbors
was selected as three.
Deep Neural Network (DNN)
: DNN is a kind of artificial neural network, which
receives a variety of predictors as inputs from the input layer, and by training the neurons
in hidden layers, the final air quality inference results are produced in the output layer.
With a large number of training samples, DNN often has excellent performance, but may
also need more computing resources. We used DNN as one of the baselines to evaluate
its time-cost and accuracy in the air quality inference task. The DNN model in our study
was developed based on the “Keras” package in Python, and there is one input layer
(64 neurons) and two hidden layers (32 and 16 neurons).
Random Forest (RF)
: RF is composed of multiple classification or regression trees.
The input predictors are randomly split into each tree using a bootstrap method, and the
data in each tree are employed to train the prediction model. It tends to have a lower
risk of model overfitting and we chose it as one of the baselines. The RF model in our
Int. J. Environ. Res. Public Health 2022,19, 8005 8 of 15
study was developed based on the “sklearn” package in Python, and n_estimators = 100,
min_samples_split = 2 were used.
XGBoost
: As one of the most popular algorithms in regression tasks, its tree structure
often achieves the better performance in both time cost and accuracy. Meanwhile, the
feature importance from XGBoost is helpful for the understanding of the inferred results.
For the XGBoost model, it was developed based on the “xgboost” package in Python, and
n_estimators = 100, learning_rate = 0.08, max_depth = 7,
min_child_weight = 3,
colsam-
ple_bytree = 1, and subsample = 1 were used.
The air pollutant concentrations from the adjacent micro stations at the same hour,
meteorological data, and land-use data were taken as input variables. For categorical
variables (e.g., weather, land-use data), we used the one-hot encoding to transform them
into feature vectors. In total, 70% of the data were used as the training set and the rest, 30%,
as the test set. Finally, we tested the inference performance of three important air pollutants
(PM
2.5
, CO, and SO
2
) in winter, and all the pollutants’ data were analyzed following the
procedures in Section 3.1. The performance of each method on the test set is shown in
Table 2.
Table 2. Model performance comparison.
PM2.5 CO SO2
Methods RMSE R2COR RMSE R2COR RMSE R2COR
KNN 12.710 0.653 0.814 0.524 0.659 0.816 6.426 0.618 0.794
SVR 11.666 0.708 0.851 0.518 0.668 0.858 6.135 0.652 0.844
DNN 11.171 0.732 0.860 0.452 0.747 0.865 5.607 0.709 0.842
Random Forest 11.188 0.731 0.856 0.455 0.743 0.862 5.645 0.705 0.840
XGBoost 10.999 0.740 0.861 0.445 0.754 0.869 5.537 0.716 0.846
Overall, the XGBoost model performs the best on all three metrics, and DNN, Random
Forest, and XGBoost have a similar performance. Significantly, the XGBoost model has
the fastest model computing speed, with 12 s on the training set and 0.09 s on the test
set. In contrast, the Random Forest model took 1 min 31 s on the training set and 3.12 s
on the test set. The DNN model took the longest time, 24 min 55 s, on the training set,
and 3.8 s on the test set. The parallelized tree construction may be one of the reasons
that the XGBoost model has the lowest time cost compared to other models, and it can
greatly facilitate the model deployment in real-world practice. The XGBoost model has
good inference performance for hourly concentrations of three pollutants, which proves
that our model results are stable and robust for inferring multiple pollutants in the city.
Furthermore, Table 3shows the comparison of the impact of different predictors on the
model performance, which shows that more micro stations, as well as external data, are
beneficial for air quality inference performance.
Then, we divided the grid data in the test set into five categories based on the distance
between each grid and its nearest micro stations. It helps to understand how the dense
monitoring data affect the model performance for different grids. As shown in Figure 4a,
overall, the closer monitoring points can further improve model performance, and the
good performance comes from the grids with micro stations within a radius of 500 m
(500 m-grids)
. We then further explored the impact of the number of micro stations on
model performance for the 500 m-grids (Figure 4b). Similarly, more micro stations per-
form better for air quality inference in general. These results indicate that there are very
fine-grained spatial variations in urban air quality, and it is important to consider these
variations to improve the accuracy of air quality inference.
Int. J. Environ. Res. Public Health 2022,19, 8005 9 of 15
Table 3. The impact of different predictors on the XGBoost model performance.
PM2.5 CO SO2
Predictors RMSE R2COR RMSE R2COR RMSE R2COR
1 station 12.820 0.647 0.806 0.567 0.602 0.784 6.806 0.571 0.763
3 stations 11.414 0.721 0.849 0.469 0.727 0.853 5.879 0.680 0.825
5 stations 11.183 0.732 0.856 0.458 0.740 0.861 5.649 0.705 0.840
7 stations 11.093 0.736 0.858 0.452 0.747 0.864 5.569 0.713 0.844
10 stations 11.055 0.738 0.859 0.449 0.750 0.866 5.550 0.715 0.846
10 s+m111.045 0.738 0.859 0.449 0.750 0.866 5.545 0.715 0.846
10 s+l211.021 0.739 0.860 0.447 0.753 0.868 5.538 0.716 0.846
10 s+m+l10.999 0.740 0.861 0.445 0.754 0.869 5.537 0.716 0.846
1Meteorological data; 2Land-use data.
Int. J. Environ. Res. Public Health 2022, 19, 8005 9 of 15
Then, we divided the grid data in the test set into five categories based on the distance
between each grid and its nearest micro stations. It helps to understand how the dense
monitoring data affect the model performance for different grids. As shown in Figure 4a,
overall, the closer monitoring points can further improve model performance, and the
good performance comes from the grids with micro stations within a radius of 500 m (500
m-grids). We then further explored the impact of the number of micro stations on model
performance for the 500 m-grids (Figure 4b). Similarly, more micro stations perform better
for air quality inference in general. These results indicate that there are very fine-grained
spatial variations in urban air quality, and it is important to consider these variations to
improve the accuracy of air quality inference.
Figure 4. The impact of dense monitoring data on model performance: (a) by different distances
between each grid and its nearest micro stations; (b) by different numbers of micro station.
3.3. Mapping Citywide Air Quality at a High Resolution
3.3.1. Continuous Variations in the Short Term
Figure 5 shows the spatial and temporal distribution of PM2.5 pollution measured
continuously for 24 h (8 February 2022, a typical weekday) in Lanzhou, inferred by the
best-performing XGBoost model. According to the hourly inference results, the citywide
PM2.5 concentration remained at a relatively low level between 01:00 and 07:00, which is
consistent with the fact of suspension of industrial and human activities during midnight.
The PM2.5 concentration in the core area began to rise from 11:00, and at 13:00, we can
clearly identify a pollution hotspot in Xigu district and its dissipation within four hours.
This pollution hotspot inferred by our model is also consistent with a maximum
observation of 136 μg/m3 and an average of 96.46 μg/m3 reported by micro stations in the
area. Our results are able to show the citywide air quality at a 500 m × 500 m × 1 h
resolution, which is useful for assessing air pollution exposure at multiple locations
within a 500 m × 500 m grid. Several studies, as well as our results, have shown that urban
air quality has obvious variations at both small temporal and spatial scales [5,8,19].
Therefore, it is of great significance for urban fine-management and health research to
infer urban air quality at the smallest possible scale.
Figure 4.
The impact of dense monitoring data on model performance: (
a
) by different distances
between each grid and its nearest micro stations; (b) by different numbers of micro station.
3.3. Mapping Citywide Air Quality at a High Resolution
3.3.1. Continuous Variations in the Short Term
Figure 5shows the spatial and temporal distribution of PM
2.5
pollution measured
continuously for 24 h (8 February 2022, a typical weekday) in Lanzhou, inferred by the best-
performing XGBoost model. According to the hourly inference results, the citywide PM
2.5
concentration remained at a relatively low level between 01:00 and 07:00, which is consistent
with the fact of suspension of industrial and human activities during midnight. The PM
2.5
concentration in the core area began to rise from 11:00, and at 13:00, we can clearly identify
a pollution hotspot in Xigu district and its dissipation within four hours. This pollution
hotspot inferred by our model is also consistent with a maximum observation of 136
µ
g/m
3
and an average of 96.46
µ
g/m
3
reported by micro stations in the area. Our results are able
to show the citywide air quality at a 500 m
×
500 m
×
1 h resolution, which is useful for
assessing air pollution exposure at multiple locations within a
500 m ×500 m
grid. Several
studies, as well as our results, have shown that urban air quality has obvious variations
at both small temporal and spatial scales [
5
,
8
,
19
]. Therefore, it is of great significance
for urban fine-management and health research to infer urban air quality at the smallest
possible scale.
Int. J. Environ. Res. Public Health 2022,19, 8005 10 of 15
Int. J. Environ. Res. Public Health 2022, 19, 8005 10 of 15
Figure 5. Hourly PM2.5 concentration distribution on a typical weekday (8 February 2022) in
Lanzhou, as inferred by our model.
3.3.2. Long-Term Distribution
Figure 6 depicts the long-term average distribution of PM2.5, CO, and SO2 pollution
in the study area inferred by our model. Obviously, there was a great difference in the
long-term distribution of air pollutants in different areas. Xigu district has a higher PM2.5
level, which may be closely related to the intensive industrial activities. In contrast, the
Anning district and the northern suburbs of the Chengguan district have a lower PM2.5
level. For CO pollution, the Chengguan district and the southern Qilihe district has higher
pollution levels. This may be due to intensive vehicle emissions in these areas. The SO2
pollution is mainly concentrated in the west of the Qilihe district, and hourly
concentration observation in this area also shows the same distribution trend. This
suggests that SO2 pollution is likely to be closely related to specific local emission sources.
The long-term inferred results from our method can provide important information for
urban management and policy making.
Figure 5.
Hourly PM
2.5
concentration distribution on a typical weekday (8 February 2022) in Lanzhou,
as inferred by our model.
3.3.2. Long-Term Distribution
Figure 6depicts the long-term average distribution of PM
2.5
, CO, and SO
2
pollution
in the study area inferred by our model. Obviously, there was a great difference in the
long-term distribution of air pollutants in different areas. Xigu district has a higher PM
2.5
level, which may be closely related to the intensive industrial activities. In contrast,
the Anning district and the northern suburbs of the Chengguan district have a lower
PM
2.5
level. For CO pollution, the Chengguan district and the southern Qilihe district
has higher pollution levels. This may be due to intensive vehicle emissions in these
areas. The SO
2
pollution is mainly concentrated in the west of the Qilihe district, and
hourly concentration observation in this area also shows the same distribution trend. This
suggests that SO
2
pollution is likely to be closely related to specific local emission sources.
The long-term inferred results from our method can provide important information for
urban management and policy making.
Int. J. Environ. Res. Public Health 2022,19, 8005 11 of 15
Int. J. Environ. Res. Public Health 2022, 19, 8005 11 of 15
Figure 6. Average concentrations inferred by our model during 8–12 February 2022 in Lanzhou city:
(a) PM2.5; (b) CO; (c) SO2.
3.3.3. More Monitors Are Always Better?
Figure 4 shows the impact of micro station distribution on inference performance for
a given grid. However, it is not clear how many deployed stations are sufficient for a
particular city to achieve acceptable performance. Therefore, we further explored the
impact of different monitoring network densities on the citywide air quality mapping
results. Specifically, we randomly selected 10%, 25%, 50%, and 75% from 448 micro
stations to map citywide PM2.5 pollution based on our model. As shown in Figure 7, with
the number of stations decreasing, the inference performance for PM2.5 pollution
gradually decreases. Specifically, it is difficult to accurately infer PM2.5 hotspots and
variations when the number of stations is 10% (less than 0.05 micro stations per square
kilometer, and still more than the number of standard stations deployed in most cities).
This is mainly because without the dense monitoring data, the model inputs for each grid
are largely similar without significant variations. This suggests that more monitoring data
are always beneficial, and there is a trade-off between the monitoring costs and the
inference accuracy, as fewer micro stations can still provide rough inferences about the air
quality variations. Therefore, for those areas in which deploying LCS networks is
considered, they can decide on the monitoring network density based on their budget and
the severity of the pollution.
Figure 6.
Average concentrations inferred by our model during 8–12 February 2022 in Lanzhou city:
(a) PM2.5; (b) CO; (c) SO2.
3.3.3. More Monitors Are Always Better?
Figure 4shows the impact of micro station distribution on inference performance
for a given grid. However, it is not clear how many deployed stations are sufficient for
a particular city to achieve acceptable performance. Therefore, we further explored the
impact of different monitoring network densities on the citywide air quality mapping
results. Specifically, we randomly selected 10%, 25%, 50%, and 75% from 448 micro
stations to map citywide PM
2.5
pollution based on our model. As shown in Figure 7,
with the number of stations decreasing, the inference performance for PM
2.5
pollution
gradually decreases. Specifically, it is difficult to accurately infer PM
2.5
hotspots
and variations when the number of stations is 10% (less than 0.05 micro stations per
square kilometer, and still more than the number of standard stations deployed in
most cities). This is mainly because without the dense monitoring data, the model
inputs for each grid are largely similar without significant variations. This suggests
that more monitoring data are always beneficial, and there is a trade-off between the
monitoring costs and the inference accuracy, as fewer micro stations can still provide
rough inferences about the air quality variations. Therefore, for those areas in which
deploying LCS networks is considered, they can decide on the monitoring network
density based on their budget and the severity of the pollution.
Int. J. Environ. Res. Public Health 2022,19, 8005 12 of 15
Int. J. Environ. Res. Public Health 2022, 19, 8005 12 of 15
Figure 7. Citywide PM
2.5
pollution mapping results on 8 February 2022 by different station densities.
4. Discussion
In recent years, there has never been a bigger need for user-focused urban air quality
monitoring services in support of safe, healthy, and resilient cities [5,40]. Especially for
industrial cities such as Lanzhou, air quality monitoring is particularly important to guide
urban management, public decision making, and health studies. In fact, in our results, the
Xigu district is more prone to severe PM
2.5
pollution than other regions, which may be
closely related to its role as an industrial cluster. This pattern of pollution distribution has
been confirmed by other studies [41,42]. There are significant differences in the long-term
distribution of multiple pollutants, which may be due to the uneven distribution of
emission sources in the city (e.g., industrial emissions are mainly from Xigu district). For
Lanzhou, as a city in northern China, the frequent heating activities in winter have a
significant influence on air quality variations. Another study in Krakow also
demonstrated this phenomenon [38]. Meanwhile, in our results, land-use information has
an obvious importance for air quality inference, which is consistent with the findings of a
study on the relationship between land use and air quality in Lanzhou city [43].
Our results show the obvious variations in urban air quality at a finer temporal and
spatial resolution, which may be closely related to the complex urban structure, human
activities, or multiple emission sources [8,44]. The high-resolution air quality inferences
may be useful for multicenter health studies with highly dense urban populations. A
variety of methods have been used for urban air quality inference, but still lack sufficient
spatio-temporal coverage, resolution, and sustainability to meet social needs [5,14,19,45].
The lack of sufficient monitoring data and stable monitoring equipment may be the reason
for this progress being hindered. In addition, compared with the dense monitoring
network in our study, it is difficult to continuously model spatial correlation of air quality
between adjacent areas due to the sparse distribution of air quality monitoring stations.
Thus, they may ignore a lot of spatial information, leading to inaccurate inference. Our
method can complement atmospheric studies to provide both a high temporal and spatial
resolution of multiple important pollutants in the city, which cannot be differentiated in
previous studies.
For urban air quality mapping, more community efforts are needed. Our method can
be applied to other similar cities or regions for air quality inference studies. They can fine-
tune our model to infer the air quality in the target city by using their monitoring data.
Figure 7.
Citywide PM
2.5
pollution mapping results on 8 February 2022 by different station densities.
4. Discussion
In recent years, there has never been a bigger need for user-focused urban air quality
monitoring services in support of safe, healthy, and resilient cities [
5
,
40
]. Especially for
industrial cities such as Lanzhou, air quality monitoring is particularly important to guide
urban management, public decision making, and health studies. In fact, in our results,
the Xigu district is more prone to severe PM
2.5
pollution than other regions, which may
be closely related to its role as an industrial cluster. This pattern of pollution distribution
has been confirmed by other studies [
41
,
42
]. There are significant differences in the long-
term distribution of multiple pollutants, which may be due to the uneven distribution
of emission sources in the city (e.g., industrial emissions are mainly from Xigu district).
For Lanzhou, as a city in northern China, the frequent heating activities in winter have a
significant influence on air quality variations. Another study in Krakow also demonstrated
this phenomenon [
38
]. Meanwhile, in our results, land-use information has an obvious
importance for air quality inference, which is consistent with the findings of a study on the
relationship between land use and air quality in Lanzhou city [43].
Our results show the obvious variations in urban air quality at a finer temporal and
spatial resolution, which may be closely related to the complex urban structure, human
activities, or multiple emission sources [
8
,
44
]. The high-resolution air quality inferences
may be useful for multicenter health studies with highly dense urban populations. A
variety of methods have been used for urban air quality inference, but still lack sufficient
spatio-temporal coverage, resolution, and sustainability to meet social needs [
5
,
14
,
19
,
45
].
The lack of sufficient monitoring data and stable monitoring equipment may be the reason
for this progress being hindered. In addition, compared with the dense monitoring network
in our study, it is difficult to continuously model spatial correlation of air quality between
adjacent areas due to the sparse distribution of air quality monitoring stations. Thus, they
may ignore a lot of spatial information, leading to inaccurate inference. Our method can
complement atmospheric studies to provide both a high temporal and spatial resolution of
multiple important pollutants in the city, which cannot be differentiated in
previous studies.
Int. J. Environ. Res. Public Health 2022,19, 8005 13 of 15
For urban air quality mapping, more community efforts are needed. Our method
can be applied to other similar cities or regions for air quality inference studies. They can
fine-tune our model to infer the air quality in the target city by using their monitoring data.
This is especially important for cities that do not yet have sufficient air quality monitoring
measures. In addition, we further show the impact of station deployment density on
air quality infer performance, which can provide valuable references for air pollution
management in other cities.
Urban air quality is a complex dynamic system, which is affected by many factors. In
this study, the feature importance from the XGboost algorithm is crucial for us to efficiently
select predictors that are beneficial for model inference. In future work, more potential
effects and factors (e.g., other pollutants, traffic, and points of interest (POI)) should be
further considered, which is helpful to improve the accuracy and reliability of the air quality
inferences. Furthermore, it is also worth studying the influence relationship between the
different pollutants by their inferred results.
5. Conclusions
In this paper, we proposed a method for high-resolution urban air quality mapping
based on dense monitoring data and machine learning techniques. We applied the method
in Lanzhou City using the air quality monitoring data from 448 micro stations in winter.
The results
show that the XGBoost model we developed can accurately infer the spatio-
temporal distribution of and variations in urban air pollutants at a
500 m ×500 m ×1 h
resolution, with an R
2
value of 0.740 for PM
2.5
, 0.754 for CO, and 0.716 for SO
2
.
The inferred
short-term variations in PM
2.5
missions clearly identify pollution hotspots and their tran-
sitions throughout the day. Meanwhile, the inferred long-term distribution of multiple
pollutants is significantly different, which may be due to the uneven distribution of emis-
sion sources in the city. We also compared the impact of the distance and density of
station deployment on air quality inference performance. The results indicate that more
stations are always beneficial as they provide more spatial information. However, one also
needs to consider the balance between monitoring costs and inference accuracy in real
urban management.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/ijerph19138005/s1, Figure S1: The distribution number of micro
stations in each administrative district; Figure S2: The regional average of air pollutant concentrations
during study period; Table S1: Air pollutants monitoring data from micro station No. 12395 on
25 October 2021; Table S2: Experiment environment; Table S3: Device parameters for air quality
monitoring; Table S4: Device performance indicators of particulate matters monitoring; Table S5:
Device performance indicators of gaseous pollutants monitoring.
Author Contributions:
Conceptualization, R.G. and Q.Z.; methodology, R.G.; resources, Q.Z.; soft-
ware, Z.P. and F.W.; supervision, Q.Z.; validation, Y.Q., B.Z. and Z.P.; visualization, R.G. and S.W.;
writing—original draft, R.G.; writing—review and editing, Y.Q., B.Z., F.W. and S.W. All authors have
read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Gansu Academy of Eco-environmental Sciences of China:
Research on water quality prediction model of Yellow River Basin based on deep learning, and the
National Natural Science Foundation of China: No. 71764025.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
We sincerely thank the reviewers for their helpful comments and suggestions
about our manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
Int. J. Environ. Res. Public Health 2022,19, 8005 14 of 15
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