Method for identifying air pollution sources based on aerosol retrieval and glowworm swarm algorithm
Techniques for identifying air pollution sources based on aerosol retrieval and a glowworm swarm algorithm are described herein. According to these techniques, a satellite remote sensing image is obtained and a wind speed vector is obtained from the satellite remote sensing image. A GSO algorithm is applied, introducing a glowworm similarity correction factor obtained from an attribute value and a wind speed and wind direction correction factor obtained from the wind speed vector, and pollution sources are identified.
1. A method for identifying air pollution sources based on aerosol retrieval and a glowworm swarm algorithm, comprising steps of:
S1: acquiring a satellite remote sensing image and a digital map of a corresponding region;
S2: acquiring a wind direction and a wind speed of the corresponding region of the satellite remote sensing image, to obtain a wind speed vector {right arrow over (speed)} wind ;
S3: retrieving (Aerosol Optical Depth) AOD values from the satellite remote sensing image, to obtain an AOD result image;
S4: gridding the AOD result image into M×N image blocks, calculating an average AOD value of pixels included in each image block; gridding the digital map of the corresponding region into M×N image blocks, obtaining coordinates E k of an image block corresponding to an enterprise k based on enterprise coordinates, where k=1, 2, . . . , K, and K represents the number of enterprises;
S5: tracing pollutant sources using a method based on (Glowworm Swarm Optimization) GSO, which comprises steps of:
S5(1): setting an initial luciferin level L i (0) of each glowworm to be the same, taking coordinates of each pixel block as an initial location x i (0) of the glowworm, taking an average AOD value corresponding to the pixel blocks as an attribute value A i of the glowworm, and setting an initial value r d i (0) of a neighborhood of each glowworm, i=1, 2, . . . , Q, Q=M×N;
S5(2): setting an iteration number t=1;
S5(3): calculating a luciferin level of a glowworm of a t-th generation according to the following equation:
L i ( t )=(1−ρ) L i ( t− 1)+γ F ( x i ( t ))
where t is the iteration number, i is an index of the glowworm, L i (t) is a luciferin level of a glowworm i of the t-th generation, L i (t−1) is a luciferin level of the glowworm i of a (t−1)-th generation, ρ is a luciferin decay constant, ρ∈(0,1), γ is a luciferin enhancement constant, F(x i (t)) is an objective function value of the glowworm i at a current location x i (t), and the objective function is calculated according to the following equation:
F
(
x
i
(
t
)
)
=
∑
k
=
1
k
1
d
ik
2
(
t
)
where d ik (t) is a distance between the glowworm i at the current location x i (t) and the coordinates E k of the enterprise k;
S5(4): for each glowworm i, respectively calculating a moving probability p ij (t) to another glowworm j in a neighborhood N i (t) thereof; selecting the glowworm j, to which the glowworm i is to move for this time, using a roulette rule, and marking with an index j′, and then updating the location of the glowworm i according to the following equation:
x
i
(
t
+
1
)
=
x
i
(
t
)
+
s
(
x
j
′
(
t
)
-
x
i
(
t
)
x
j
′
(
t
)
-
x
i
(
t
)
)
+
Δ
attribute
+
Δ
wind
where x j′ (t) is a location of the glowworm j′ of the t-th generation, S is a moving step size, and ∥ ∥ represents solving an Euclidean norm;
Δ attribute is a glowworm similarity correction factor calculated as follows: firstly in a neighborhood N i (t) of the glowworm i, a glowworm j* with a closest attribute value A i to the glowworm i is searched and then Δ attribute is calculated:
Δ
attribute
=
s
a
·
(
x
j
*
(
t
)
-
x
i
(
t
)
x
j
*
(
t
)
-
x
i
(
t
)
)
where s a is a predetermined step size, x j* (t) is a location of the glowworm j* of the t-th generation;
Δ wind is a wind speed and wind direction correction factor, and calculated as: Δ wind =s w ·{right arrow over (speed)} wind , where s w is a scaling factor;
S5(5): in condition that the iteration number t=T, T being a maximum predetermined iteration number of times, taking a current location of each glowworm as a source location X i 0 thereof, and terminating the iteration; otherwise proceeding to step S5(6);
S5(6): updating a neighborhood radius r d i (t+1) of each glowworm, then letting t=t+1, and returning to step S5(3);
S6: obtaining a radius R k of a pollution coverage area of the enterprise according to the coordinates E k of the image block corresponding to the enterprise;
S7: according to the radius R k of the pollution coverage area and the source location x i 0 of each glowworm, taking the enterprise k as an enterprise producing pollutants corresponding to the glowworms in the pollution coverage area thereof, calculating an AOD corresponding to the glowworms in the pollution coverage area thereof;
identifying the enterprise k as a pollution source;
determining a pollution index of the enterprise k, wherein the pollution index represents a pollution level of the enterprise k; and
using the determined pollution index to evaluate a pollution situation of the enterprise k.
2. The method for identifying air pollution sources according to claim 1 , wherein the moving probability p ij (t) in the step S5(4) is calculated as follows:
p
ij
(
t
)
=
L
j
(
t
)
-
L
i
(
t
)
∑
k
∈
N
i
(
t
)
[
L
k
(
t
)
-
L
i
(
t
)
]
where a neighborhood set is N i (t)={j:d ij (t)<r d i (t); L i (t)<L j (t)}, d ij (t) is a distance between current locations of the glowworms i and j, r d i (t) is a neighborhood radius of the glowworm i of the t-th generation, 0<r d i (t)≤r s , r s is a sensor radius of the glowworm, and L j (t) is a luciferin level of the glowworm j of the t-th generation.
3. The method for identifying air pollution sources according to claim 1 , wherein the neighborhood radius r d i (t+1) in the step S5(6) is calculated as follows:
r d i ( t+ 1)=min{ r s ,max{0, r d i ( t )+β( n t −|N i ( t )|)}}
where β is a constant parameter, n t represents an area number control parameter.
4. The method for identifying air pollution sources according to claim 1 , wherein in the step S6 the radius R k is calculated as follows: as to the enterprise k, firstly searching for an enterprise k′ closest thereto, and calculating a distance D kk′ between the two enterprises, R k =0.5D kk′ .
5. The method for identifying air pollution sources according to claim 1 , wherein the pollution index is a total pollution gross PG k , and calculated as:
PG
k
=
∑
w
=
1
W
k
X
w
0
where W k represents the number of glowworms in the pollution coverage area of the enterprise k, X w 0 represents an AOD value of a glowworm win the pollution coverage area of the enterprise k.
6. The method for identifying air pollution sources according to claim 1 , wherein the pollution index is a pollution intensity PI k , and calculated as:
PI
k
=
1
W
k
∑
w
=
1
W
k
X
w
0
.
7. The method for identifying air pollution sources according to claim 1 , wherein the pollution index is area normalization pollution ANP k , and calculated as:
ANP
i
=
∑
k
n
a
k
ln
(
area
i
)
=
PG
i
ln
(
area
i
)
where area k is the pollution coverage area of the enterprise k.