Descriptive statistical analysis of all parameters
The annual average concentrations of air pollution, geo-meteorological, and social parameters showed in Table 2. The mean annual average concentration of PM2.5 (83.98 μg/m3), NO2 (427.29 nm2), SO2 (89.18 ppm), CO (93.10 ppm), and O3 (238.40 ppm) were above the national level threshold. The minimum and maximum range of digital elevation model and land surface temperature were 6–14 meters and 20–270C respectively. The mean annual rainfall and wind speed were 275 mm and 0.96 mph respectively in the study area. In the social parameters, 0–1878 per person found per square kilometer while the mean poverty and literacy rates were 70% and 74% respectively.
Table 2 The annual average of the key variables used in this study
Theme
|
Variables
|
Mean
|
Std Error
|
Standard Deviation
|
Range
|
Min
|
Max
|
Sum
|
Air pollution parameters
|
PM2.5
|
83.98
|
0.04
|
0.40
|
2.05
|
83.06
|
85.11
|
7222.66
|
NO2
|
427.29
|
1.86
|
17.21
|
83.95
|
373.87
|
457.82
|
36746.62
|
AOT
|
0.45
|
0.00
|
0.01
|
0.05
|
0.42
|
0.46
|
38.46
|
SO2
|
89.18
|
1.02
|
9.43
|
41.87
|
47.24
|
89.11
|
5089.84
|
CO
|
93.10
|
0.02
|
0.15
|
0.78
|
92.94
|
93.72
|
8006.90
|
Water vapor
|
2.39
|
0.00
|
0.00
|
0.00
|
2.39
|
2.39
|
205.54
|
O3
|
238.40
|
0.02
|
0.23
|
0.87
|
237.96
|
238.83
|
20502.62
|
Geo-meteorological parameters
|
DEM
|
9.83
|
0.25
|
2.27
|
8.02
|
6.20
|
14.22
|
845.33
|
Wind pressure
|
2.00
|
0.01
|
0.07
|
0.26
|
1.88
|
2.14
|
172.37
|
LST
|
26.17
|
0.01
|
0.10
|
0.46
|
25.87
|
26.33
|
2250.39
|
Rainfall
|
275.89
|
2.13
|
19.79
|
72.19
|
240.05
|
312.24
|
23726.29
|
Wind speed
|
0.56
|
0.00
|
0.04
|
0.10
|
0.50
|
0.60
|
48.26
|
Social parameters
|
Pop density
|
336.15
|
37.79
|
350.42
|
1878.00
|
0.00
|
1878.00
|
28909.32
|
Brickfield
|
60.39
|
2.33
|
21.62
|
96.52
|
15.48
|
112.00
|
5193.29
|
Poverty
|
69.73
|
0.23
|
2.16
|
10.27
|
64.62
|
74.89
|
5996.87
|
Land use
|
2.91
|
0.11
|
1.04
|
4.00
|
1.00
|
5.00
|
250.00
|
Literacy
|
73.81
|
0.71
|
6.58
|
29.00
|
57.00
|
86.00
|
6348.00
|
Geographical Weighted Regression (GWR) parameters
Three Geographical Weighted Regression models were executed in order to understand the statistics and spatial relationships between the COVID–19 infection rate (dependent variable) and the 17 independent variables of air pollution, geo-meteorological and social data. After running these multi-regression models, significant model fitting results such as estimated coefficient, standard error, T, p-value, and r2 in the 3 sets of parameters are listed (Table 3).
In the air pollution data, PM2.5 (0.64), NO2 (0.11), AOT (0.81), SO2 (0.20), and O3 (2.25) parameters had positive values while CO (–0.56) and water vapor (–3.04) had negative coefficient values. Standard error calculates the distance between each of the data points and its predicted value in a model. The model used in this study estimated a very small standard error in all independent variables (less than 1). There were only 2 negative parameters CO (–1.9) and water vapor (–3.89) found in the T result. Among the 7 air pollution parameters, 5 were statistically significant (r2) to the COVID–19 infection rate. PM2.5, AOT, and O3 statistically posed a significant positive effect on the COVID–19 infection rate at a 95% confidence level. On the other hand, CO and wind vapor had negative coefficient with the COVID–19, but interestingly both of them were statistically significant at p–0.01. These results assumed that a 1 unit increase in the 5 parameters may increase the COVID–19 attack or mortality rates in Dhaka City. This model calculated a strong r2 (0.84), which indicates that 84% of the COVID–19 attack is explained by the 7 air pollution parameters.
5 parameters were used for the geo-meteorological data, of which only water pressure (–0.260) had a negative coefficient value. All other parameters like DEM (0.361), LST (0.236), rainfall (0.439), and wind speed (0.212) had a positive influence on the dependent variable by showing a robust r2 (0.73). This model explained about 73% variation in the independent variables responsible for COVID–19 infection rates. All values extracted from the standard error showed in the normal range while the water pressure (–1.60) was negative in T result. Moreover, among the 5 geo-meteorological parameters, DEM (p<0.01)), LST (p<0.04), rainfall (p<0.07), and wind speed (p<0.03) were statistically significant with the COVID–19 infection rate. Therefore, these parameters have the potential to influence the infection of COVID–19 in Dhaka.
Poverty had negative values in the estimated coefficient (–0.283) and T result (–2.382), although it was statistically significant (p<0.01).. On the other hand, population density, brickfield density, land use, and literacy rate had positive results in both the estimated coefficients and T results. These 4 parameters showed a very small standard error, showing these accounted for 70% of the COVID–19 infection rate (r2 = 0.70) in this model. However, the other 2 social parameters like population density (p<0.01) and brickfield (p<0.02) were statistically significant in this regression model. Land use and literacy rate were not statistically significant.
Table 3 Model fitting results of Geographical Weighted Regression Model
Theme
|
Parameters
(Independent)
|
Model fitting results (COVID-19 as dependent variable)
|
Estimated coefficient
|
Standard error
|
T
|
p-value
|
R2
|
Air pollutant
|
PM2.5
|
0.645
|
0.295
|
2.187
|
0.029
|
.84
|
NO
|
0.112
|
0.305
|
0.368
|
0.713
|
AOT
|
0.816
|
0.322
|
2.531
|
0.011
|
SO2
|
0.200
|
0.416
|
0.481
|
0.630
|
CO
|
-0.562
|
0.296
|
-1.900
|
0.057
|
Wind Vapor
|
-3.043
|
0.784
|
-3.881
|
0.015
|
O3
|
2.257
|
0.556
|
4.059
|
0.013
|
Geo-meteorological
|
DEM
|
0.361
|
0.105
|
3.428
|
0.012
|
.73
|
Wind Pressure
|
-0.260
|
0.162
|
-1.604
|
0.015
|
LST
|
0.236
|
0.126
|
1.883
|
0.040
|
Rainfall
|
0.439
|
0.163
|
2.693
|
0.017
|
Wind speed
|
0.212
|
0.102
|
2.077
|
0.038
|
Social
|
Pop density
|
0.318
|
0.116
|
2.738
|
0.016
|
.70
|
Brickfield
|
0.212
|
0.095
|
2.233
|
0.026
|
Poverty
|
-0.283
|
0.119
|
-2.382
|
0.017
|
Land use
|
0.165
|
0.104
|
1.591
|
0.112
|
Literacy
|
0.047
|
0.098
|
0.481
|
0.630
|
Spatial Relationship between the COVID–19 infection rate and air pollution, geo-meteorological and social parameters
In order to understand the different spatial relationships among these factors through the advanced statistical lens, r2, influence, and the p values were derived from the Geographically Weighted Regression (GWR) model. The minimum and maximum r2 values were 0.30 and 0.84 in the air pollution parameters respectively (Fig. 3, Map a). The mean value was 0.39 which was mostly found in the south and middle zones of the study area. On the other hand, the mean r2 value (0.28) of geo-meteorological parameters was found in the middle, middle eastern, and southern parts of the study area keeping minimum and maximum values 0.18 and 0.73 respectively (Fig. 3, Map c). The social parameters were important predictors for establishing a relationship with the COVID–19 infection rate. The mean r2 value (0.27) was found in the middle, middle eastern, and north-western parts of the study area (Fig. 3, Map b). Whilst its standard deviation was 0.04, the minimum and maximum values were 0.21 and 0.70 respectively.
The higher influence values of independent variables predict a good explanation for the dependent variable. In air pollution parameters, a number of influence zones in the north, eastern, western, and a few in the southern parts were found (Fig. 3, Map a). Except for the middle part of the study area, the influence statistics was quite significant across the study area keeping 0.13 and 0.03 in mean and standard deviation respectively. The influence of geo-meteorological parameters was significantly higher in the study area; except Cantonment, Gulshan, Ramna, Kafrul, Shahbag, Bangshal, Chakbazar, Gendaria and Shyampur areas (Fig. 3, Map c). The minimum and maximum values of geo-meteorological parameters were 0.07 and 0.13 respectively while its mean value was 0.09. Social parameters were quite influential in the north-eastern and southern parts. The minimum and maximum values of social parameters were 0.06 and 0.21 respectively, while the mean value was 0.10 (Fig. 3, Map b). A strong influence of social parameters was found in the southern part of the city.
The p-value is an important parameter in this study. There were some strong relationships between COVID–19 infection rates data and air pollution, geo-meteorological, and social parameters. Strong statistical evidence of p values in PM2.5 (<0.02), AOT (<0.01), CO (<0.05), water vapor (<0.01), and O3 (<0.01) in the air pollution parameters were found. Most of these parameters and their correlated p-values were found in the middle, south, south-western, and south-eastern parts of the study area. Interestingly, O3, AOT, and wind vapor were found their strong prevalence over the whole study area (Fig. 5).
A number of geo-meteorological parameters selected in this model had some positive relationships with the COVID–19 infection rate. Digital elevation model (DEM), land surface temperature (LST), rainfall and wind speed had statistically significant p-values of <0.01, <0.04, 0.01 and <0.03 respectively. Fig. 6. showed a spatial distribution of r2 map in all of the geo-meteorological parameters in where DEM, LST, wind speed, and wind pressure were found in the south, middle, middle eastern and southern parts in the area, respectively. Rainfall had a great statistical influence on the dependent variable, which covered 90% of the study area.
Population density (p<0.01), brickfield (p<0.02),, and poverty (p<0.01) had significant roles on COVID–19 infection rate in the study area. In terms of the geographical distribution of population density and poverty, about 90% of areas were covered by these factors (Fig. 7). On the other hand, brickfield was highly visible in the southern part of the study area in which the COVID–19 infection rate was higher. Land use and literacy rate were not statistically significant, but these had a positive coefficient with the dependent variable.