DOI: 10.5281/zenodo.14236193
title: Machine learning for the assessment and mitigation of SMOG hazard in low-middle income developing countries

description: This is the first statistical research based on of five years of smog data from 2019 to 2023 in Lahore, using machine learning techniques. This research used machine learning techniques, namely using Python programming for data analysis. Python-based analyses can effectively assess regions with poor air quality and high pollution levels. This allows targeted interventions and restrictions to safeguard community health and avert respiratory disorders. 
description: Table 1. Demographic composition of sample






Variable                                 



Frequency  



Percentage (%)





Gender

·      Female

·      Male



12

18



40

60





Age 

·      16-35

·      36-40

·      40-50

·      >50



 

10

7

6

7



 

33.3

23.3

20

11.6





Education level

·      Illiterate

·      Pre-school

·      High school 

·      College

·      University and other



 

3

5

5

6

11



 

10

16.6

16.6

20

36.6





Socioeconomic status

·      Lower class

·      Middle class

·      Upper class



 

3

22

5



 

10

73.3

16.6





 
description: Table 2. Health impacts of smog on population








Symptoms



Yes



No





Respiratory problem



26 (86%)



4 (13.3%)





Sore throat, eye problem



12 (40%)



18 (60%)





Fever or chills



5 (17%)



25 (83%)





Cough



27 (90%)



3 (10%)





Congestion or runny nose



7 (23%)



23 (76%)





Chronic obstructive pulmonary disease (COPD)



 

0



 

25 (83%)
description: Table 3. Concentration of particulate matter (PM2.5 and PM10) during smog from 2019-2023 (IQ Air 2021; EPD 2021)








Average monthly analysis



Avg. PM2.5 and PM10 (µg/m3)

in 2019



Avg. PM2.5 and PM10 (µg/m3)

in 2020



Avg. PM2.5 and PM10 (µg/m3)

in 2021



Avg. PM2.5 and PM10 (µg/m3)

in 2022



Avg. PM2.5 and PM10 (µg/m3)

in 2023





September



60 and 67



 44and 48



87.5 and 101



64.5 and 95



77and 100





October



97 and 70



60 and 48



112 and 104



123.2 and 130



150 and 165





November



99 and 73



115 and 100



122 and 107



190.5 and 133



157 and 174





December



112 and 100



131 and 112



135 and 125



192.9 and 141



208 and 217
description: Table 4. Statistical trend of PM2.5 pollutant from 2019 to 2023






 


 



 





Index

 


Yearly report

 


 





2019



2020



2021



2022



2023





Standard deviation



 

22.3



 

42.0



 

20.0



 

61.3



 

53.9





Min



60



44



87.5



64.5



77





Mean



92



87.5



114.1



142.7



148





Max



112



131



135



192.9



208





75%



102.2



119



125.2



191.1



169.7





50%



98.0



87.5



117



156.6



154





25%



87.7



56



105.8



108.5



132
description: Table 5. Statistical trend of PM10 pollutant (2019-2023)

 








 

Index



Yearly report



 





2019



2020



2021



2022



2023





Standard deviation



15.1



33.8



10.7



20.3



48.3





Min



67



48



101



95



100





Mean



77.5



77



109.2



124.7



164





Max



100



112



125



141



217





75%



79.7



103



111.5



135



184.7





50%



71.5



74



105.5



131



169.5





25%



69.2



48



103.2



121



148.7