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GIS-Based Air Quality Index Spatial Model for Indian Cities

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Challenges and Solutions for Sustainable Smart City Development

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Purpose—Generation of ‘Spatial and Gridded Air Quality Index Model’ using GIS to compute the AQI using the known ground data for the locations where there are no sensors at a selected grid size. The main objectives of the model are to integrate observations, to predict the unknown values and to make provision for future development without compromising with quality.

Methodology—The model will be based on the GIS concept of point interpolation. It will use the geoprocessing tool—buffer, data management tool—join, spatial analyst tool—kriging interpolation. Inputs of the model will be the interpolated rasters of the ground measurements of the pollutants. The output will be the AQI map of the study area represented using six categories of AQI of India—‘Good’, ‘Satisfactory’, ‘Moderately Polluted’, ‘Poor’, ‘Very Poor’ and ‘Severe’.

Practical Implications and Originality—The model generated can be used for any Indian city for daily, seasonal and annual analysis. It will also help to keep a check with the annual CPCB and WHO air quality standards.

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References

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Annexure 1: Scripts

Annexure 1: Scripts

1.1 Script for Calculating PM2.5 Sub-index

Expression subindex( !Value!) Code block def subindex(x): if x>=0 and x<= 30: return (((50-0) *(x-0))/ (30-0)) +0 elif x>30 and x <= 60: return (((100-50) *(x-30))/ (60-30)) +50 elif x>60 and x<=90: return (((200-100) * (x-60)) / (90-60)) +100 elif x>90 and x<120: return (((300-200) * (x-90)) / (120-90)) +200 elif x>120 and x<=250: return (((400-300) * (x-120)) / (250-120)) +300 elif x>250: return (((500-400) * (x-250)) / (380-250)) +400

1.2 Script for Calculating PM2.5 Sub-index Class

Expression index(!SubIndex!) Code block def index(x): if x >=0 and x <=50: return "Good (0-50)" elif x >50 and x <=100: return "Satisfactory (51-100)" elif x>100 and x<=200: return "Moderately Polluted (101-200)" elif x>200 and x<=300: return "Poor (201-300)" elif x>300 and x<=400: return "Very Poor (301-400)" elif x>400 and x<=500: return "Severe (401-500)"

1.3 Script for Calculating PM10 Sub-index

Expression subindex( !Value!) Code block def subindex(x): if x>0 and x<= 50: return x elif x>50 and x <= 100: return (((100-50)*(x-50))/(100-50))+50 elif x>100 and x<=250: return (((200-100) * (x-100)) / (250-100)) +100 elif x>250 and x<350: return (((300-200) * (x-250)) / (350-250)) +200 elif x>350 and x<=430: return (((400-300) * (x-350)) / (430-350)) +300 elif x>430: return (((500-400) * (x-430)) / (510-430)) +400

1.4 Script for Calculating PM10 Sub-index Class

Expression index(!SubIndex!) Code block def index(x): if x >=0 and x <=50: return "Good (0-50)" elif x >50 and x <=100: return "Satisfactory (51-100)" elif x>100 and x<=200: return "Moderately Polluted (101-200)" elif x>200 and x<=300: return "Poor (201-300)" elif x>300 and x<=400: return "Very Poor (301-400)" elif x>400 and x<=500: return "Severe (401-500)"

1.5 Script for Calculating SO2 Sub-index Values

Expression subindex( !Value!) Code block def subindex(x): if x>=0 and x<= 40: return (((50-0) *(x-0))/ (40-0)) +0 elif x>40 and x <= 80: return (((100-50) *(x-40))/ (80-40)) +50 elif x>80 and x<=380: return (((200-100) * (x-80)) / (380-80)) +100 elif x>380 and x<800: return (((300-200) * (x-380)) / (800-380)) +200 elif x>800 and x<=1600: return (((400-300) * (x-800)) / (1600-800)) +300 elif x>1600: return (((500-400) * (x-1600)) / (3200-1600)) +400

1.6 Script for Calculating NO2 Sub-index Values

Expression subindex( !Value!) Code block def subindex(x): if x>=0 and x<= 40: return (((50-0) *(x-0))/ (40-0)) +0 elif x>40 and x <= 80: return (((100-50) *(x-40))/ (80-40)) +50 elif x>80 and x<=180: return (((200-100) * (x-80)) / (180-80)) +100 elif x>180 and x<280: return (((300-200) * (x-180)) / (280-180)) +200 elif x>280 and x<=400: return (((400-300) * (x-280)) / (400-280)) +300 elif x>400: return (((500-400) * (x-400)) / (800-400)) +400

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Munoth, N., Sharma, N. (2021). GIS-Based Air Quality Index Spatial Model for Indian Cities. In: Maheswar, R., Balasaraswathi, M., Rastogi, R., Sampathkumar, A., Kanagachidambaresan, G.R. (eds) Challenges and Solutions for Sustainable Smart City Development. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-70183-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-70183-3_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-70183-3

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