Analysis & Application of GIS Based Air Quality Monitoring- State of Art

DOI : 10.17577/IJERTV2IS121260

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Analysis & Application of GIS Based Air Quality Monitoring- State of Art

Mamta Pandeya, Varun Singha, R. C. Vaishyaa, Anoop Kumar Shuklab

a Department of GIS Cell, Motilal Nehru National Institute of Technology, Allahabad, India.

b Department of Civil Engineering, Indian Institute of Technology, Roorkee, India.


The degradation of air quality is a major environmental problem that affects many surrounding regions of industrial sites. Exposure to gaseous pollutants like SOx, NOx, Gaseous Hg, Gaseous F and particulate matter like RSPM, SPM, particulate Hg etc. cause severe health effect like respiratory, cardiovascular diseases and cardio pulmonary mortality. This paper carry out of comparative review of GIS based systems and mathematical models for air quality monitoring. Further it presents a schematic framework for the GIS based evaluation of air pollution situation in surrounding regions of industrial sites and the factors that should be taken in consideration for developing the GIS based system based on the proposed framework.

Keywords: Air quality, Air quality indices, GIS, Geostatistical Analysis, Health risk

  1. Introduction

    Air pollution todays major problem in our modern society and several factors concur to create unfavourable conditions for air pollutant dispersion. The effect of air pollution on public health depends on several factors like chemical composition of a particular pollutant, the level of concentration; health status of individuals and time of exposure. Assessment of the impact of air quality effects on plants, animals, natural ecosystems, ecosystem and human health is important. Air quality management include monitoring and analysis of pollutant concentration, spatial distribution of pollutant

    concentration, assessment of no. of environmental factors affected by air pollutants, health risk map. Application and analysis of GIS for assessment of air quality is very useful for mapping and examine the Air pollutant data. The application of GIS for air quality analysis and health risk map helps in finding out the relationship between the distribution of air quality, density of population and health risk of the population. By using geostatistical analysis functionalities of GIS relationship between long term exposure to air pollution and the development of specific chronic diseases including bronchitis, asthma and cancer can be established.

  2. Literature review

    Researchers have carried out research about spatial models that examine concentration and spatial distribution of air pollutants. Majorly they have provide feasible method using GIS and decision support system that examine spatial point pattern of air pollutants and identifies the relationship between air quality and health risk and gives better visualization and analysis possibilities. Table 1 review the research work carried out by different authors. Comparative analysis of works carried out by different authors suggested the importance of geostatistical analysis for air quality monitoring.

    Table 1. Comparative Analysis of Research Work for Air Quality Monitoring

    Author Name

    Technology used

    Type of the Application

    Information Provided


    Chattopadhyay S. et al, (2010)

    GIS Technology, Digital elevation

    model (DEM),

    Inverse Distance Interpolation (IDINT) Technique.

    Digital elevation model (DEM) & Inverse Distance Interpolation (IDINT) technique generated on the basis of Air quality Index (AQI) GIS based air pollution surface models.

    To evaluate the pre monsoon and post monsoon distribution of selected gaseous pollutants i.e. SO2, NO2 and RSPM and investigate the seasonal variation of ambient air quality status of Burdwan town using GIS approach.

    This shows the significant seasonal variation due to gaseous pollutants.


    GIS, Air dispersion

    Developed new

    Provides a relatively simple and

    The air dispersion modelling


    model, Proximity

    procedures to loosely

    feasible method for health

    exhibited advantages over

    analysis, Loose

    integrate an air

    scientists to take advantage of both

    proximity analysis and


    dispersion model,

    air dispersion modelling and GIS

    geostatistical method for

    AREMOD, and a GIS

    by avoiding the need for intensive

    environmental health

    package ArcGIS to

    programming and substantial GIS


    simulate air dispersion


    from stationary

    sources for five

    pollutants: PM10,

    PM2.5, NOX, CO and



    GIS and Spatial

    Ripleys K method

    Using Ripleys K with GIS that

    Examine the spatial point


    analysis method

    examines the spatial

    identify statistically significant

    pattern of industrial toxic

    Ripleys K,

    point pattern of

    areas of clusters and also the scales

    substances and problem of

    ISCST3 air

    industrial toxic

    at which those clusters exist.

    non-point sources with an

    dispersion model.

    substances.ISCST3 air

    analysis of the street

    dispersion model


    identify the number of

    people potentially

    affected by air toxic.



    The spatial models and

    Wide range of data collected by

    Manage all the data together


    Imagine, LIDAR.

    their extensions are

    monitoring systems and by

    with GIS model outputs to

    developed in the

    mathematical and physical

    carry out risk assessment

    framework of the

    modeling can be managed in the

    analysis and map

    ESRIs ArcGIS and

    frame of spatial models developed

    composition, spatial

    ArcView programming

    in GIS.

    database , spatial modeling

    tools The measurement

    for air quality.

    of NOX and O3 by an

    automatic monitoring

    system and data from the differential


    absorption LIDAR are

    used for investigation

    of air pollution.


    Integrated decision

    Through the

    Identify high concentrations of

    Identification of high


    support system, GIS.

    development of

    pollutants in places such as

    concentration of pollutants

    prototype software

    residential/commercial areas.

    in residential/commercial

    IMPAQT (Integrated


    Modular Program for

    Predict travel impacts on present or

    Air Quality Tools)

    future transportation systems.

    with using a

    Evaluation of existing travel


    demands on the current transport

    transportation model,

    network, and the prediction of

    an advanced

    future traffic flows for

    atmospheric dispersion

    transportation planning.

    model and a desktop

    GIS to carry out urban

    Dispersion model assess future air

    air quality assessments

    quality based on emission

    and to test traffic

    scenarios derived from


    transportation models.


    GIS, Airshed model.

    Develop a framework

    The framework identifies the

    This paper evaluate the


    for integrating land

    relationship between various

    effect on city due to air

    use, transport and

    components such as the GIS

    quality that identify the

    airshed models for

    database, the land use- transport-

    relationship between various

    evaluating the effect of

    environment module and the

    components components

    city form on air

    airshed model.

    such as the GIS database,


    the land use-transport-

    environment module and the

    airshed model



    Integrated existing

    Gives a summary of the basic road

    This integrated emission


    Software, Air quality

    emission calculation

    traffic emission model and then

    evaluation system offers

    index (AQI).

    software with a

    focuses on the design and

    entirely new ways of using

    graphical user

    implementation of the computer

    the emission model and

    interface, which

    application with the emphasis on

    gives additional

    includes a GIS

    the used component and GIS

    visualization and analysis





    GIS, Regression

    With GIS regression

    By using GIS that containing data

    This provide pollution map


    based approach.

    based approach is used

    on monitored air pollution levels,

    by estimation of NO2

    for mapping traffic

    road network, traffic volume, land


    related air pollutant

    cover, altitude assessed predicted


    pollution levels.

  3. Mathematical modelling

    In the previous section we have discussed many issues regarding air quality analysis, air quality index and health risk map and use of many technology that provide appropriate information but there are need of mathematical modelling that are applicable for mapping traffic related air pollution, estimation of air pollutant concentrations, analysis of association between air pollution and mortality, focused on the contributions of air pollutant emissions from stationary sources to the ambient air and their local impact on public health etc.

    They have discussed some mathematical models that applicable for solving traffic related air pollution, air pollutant concentrations, association between air pollution and human health effect, contributions of air pollutant emissions from stationary sources to the ambient air and their local impact on public health etc are given below in tabular form:-

    Author name



    Briggs (1997),

    Mean NO2 = 11.83 + (0.00398 Tvol300) + (0.268Land300) – (0.0355RSAlt) +


    Stepwise multiple regression analysis was return using the two compound factors (Tvol300 and Land300), together with altitude (variously transformed), topex, sitex and sampler height, against the modeled mean nitrogen dioxide concentrations.

    This equation is applicable for mapping traffic related air pollution for NO2 that compute the predicted pollution level at all unmeasured sites.

    Wong (1994)



    Z ( Xo) n i Z ( Xi)0 n i 1

    i1 and i1

    where i represent the weights assigned to each of the neighbouring values, and the sum of the weights is one.

    Compute the air pollution concentration, z at an unsampled point, x0, given a set of neighboring sampled values zi, sampled at locations denoted by xi. The interpolating relationship is given above.

    Estimate air pollutant concentrations like O3, PM10 by using four different interpolation methods (1) spatial averaging, (2) nearest neighbor, (3) inverse distance weighting, and (4) Kriging.

    Jerrett (2005)



    Develop model that can be expressed mathematically in the form-

    Developed and used Cox proportional hazards regression for analysis of

    hij s (t) = h0 s (t) j exp (xij s)

    association between air pollution and mortality.

    Where hij is the hazard function or instantaneous hazard probability of death for

    the ith subject in the jth ZCA, whereas s indicates the stratum (defined by

    sex,race and age). Here h0 s(t) is the baseline hazard function. The j are positive

    random effects representing the unexplained variation in the response among

    neighbourhoods, in this case zip code areas. Only the moments of the andom

    effects need to be specified within our modeling framework: E (j) = 1 and

    Var(j)= 2 .The vector xij represents the known risk factors for the response

    such as air pollution, smoking habits, and diet. The regression parameter vector

    is denoted by .

    Maantay, (2009)

    Calculated and compared the sub-indices for the five air pollutants at each receptor (point), and the highest sub-index is used as the SII for that point. The equation followed that of the US EPA AQI, expressed below:

    I I HI ILO (C BP ) I

    p BP BP p Lo Lo Hi Lo

    Where Ip is the sub-index for pollutant p, Cp is the concentration of pollutant p, BPHi is the top breakpoint that is greater than or equal to Cp , BPLo is the bottom breakpoint that is less than or equal to Cp, IHi is the sub-index corresponding to BPHi , ILois the sub-index corresponding to BPLo.

    They have focused on the contributions of air pollutant emissions from stationary sources to the ambient air and their local impact on public health.

    Crabbe et al.,(2000)

    Ep e (x,t)



    C( x,t ) dt

    e( x,t ) t 0

    t1 t0

    And x= home, work, and other locations identified in the GIS, t= time spent at each location identified from the environmental factor questionnaire, e = the exposure to air quality at that location, either measured or modeled, c = concentration of air quality at that point, Ep = total personal exposure.

    Modeled personal exposure of air pollutants for purpose of characterization of human exposure and dose assessment techniques.

    Chelani, (2010)

    The Oak Ridge air quality index is given by,

    ORAQI = 5.7 × s Ii1.37


    Where, Ii= Concentration of pollutants ÷ Standard level of pollutant.

    The use of an index called Oak Ridge Air Quality Index (ORAQI) based on 24 hourly average concentrations of air pollutants. This index is formulated based on the premise that the effect on environmental quality varies inversely in relation to the pollutant concentration.

    Chattopadhyay, (2010)

    If total n no of parameters were considered for air monitoring, then geometric mean of these n number of quality ratings can calculated in the following way


    where g = geometric mean ; a, b,c,d,x = different values of air quality rating; and n= number of values of air quality rating, log = logarithm.

    Air quality rating of each parameter used for monitoring is calculated in each zone.

    Joshi et al., (2010)

    used following computation to drive the air quality index of the sites under consideration:

    AQ I= ¼ RSPM SPM SO2 NO2 100

    sRSPM sSPM sSO2 sNO2

    Where sRSPM, sSPM, sSO2, and sNO2 represent the ambient air quality standards as prescribed by the Central Pollution Control Board of India and RSPM, SPM, SO2 and NO2 represent the actual values of pollutants obtained on sampling. After compiling the results, the concentration of each pollutant was converted into an AQI.

    The ambient air quality survey was carried out at four different locations with respect to SO2, NO2, SPM and RSPM, and monthly air sampling was carried out for a period 24 hrs at each of the site.

    1. q = 100 × V / Vs ; where q = quality rating ; V = observed value of parameter ; Vs = value recommended for that parameter.

    2. g antilog {(loga logb …………logx)/n}

  4. Implementation Factor

    The major implementation issues associated with air quality index and environmental health research data acquiring and integrate with the geographic data that should be selected in a way that geographic data (both spatial and associated non-spatial attribute data). Concentration and emission data of major air pollutants like RSPM, PM10, PM2.5 , NOX, CO and SO2 should be collected and can be analyze the statistical parameters of air pollutants such as average

    concentration of the air pollutants, mean monthly value of air pollutants and emission rate of air pollutants and meteorological data should be collected and then we can assess statistical parameters of meteorological condition like wind speed, wind direction, atmospheric stability, mixing height, humidity, rainfall and can be assessed the seasonal variation and with this context we can assess the human health risk assessment with the help of collection of human health data like type of disease caused by air pollution like risk of developing cancer,

    respiratory and allergy diseases and aggravates the condition of people suffering from respiratory or heart diseases and no of peoples that can be affected by these diseases and no of mortality. The air pollutants that raise health concerns and with the help of this we can be analyze data related to environmental and socio-economic factors.

    The above mention problem can be solve with using ArcGIS Geostatistical Analyst. With the help of this ArcGIS Geostatistical Analyst we can be describe the behaviour of the concentrations emitted by a group of polluting sources and analyze the behaviour and distribution of pollutants and particulate matter. This statistical technique used for the estimation, prediction and simulation of information correlated

    The implementation factors are given below in tabular form:-

    spatially. Geostatistical methods provide a tool like semivariograms that allows to explore and estimate the available information, allowing to take better decisions. The other tools are Kriging application that is possible to minimize the variance of the error prediction and it estimate the characteristics of variability and spatial correlation of the studied area.

    Geostatistical methods can be apply for health risk map. Health risk map representing the spatial distribution of respiratory symptoms and diseases that can be produced through spatial interpolation techniques. Exploratory Spatial Data Analysis can be used for understanding the properties of the spatial dataset.

    Author name

    Statistical Parameters




    Jensen, (2001)

    Danish operational street pollution model (OSPM), hourly inputs of traffic, meteorological parameters, urban background concentration, street configuration parameters.

    GIS Software (Arc View), Danish operational street pollution model (OSPM).

    Technical and cadastral digital maps, Danish national administrative databases on buildings, cadastres and populations.

    OSPM model calculates ambient hourly concentration levels of CO, NO2, NOx (NO +

    NO2), O3 and benzene.




    Average concentration of

    Resourcesat-1 satellite

    Concentration of Air

    Seasonal variation of


    the RSPM, SO2 and

    image and Geomatica

    pollutant such as

    ambient air quality

    NO2 for both

    V.10.2 software.

    RSPM, SO2 and NO2.

    status using GIS

    Premonsoon and Post

    Meteorological data.


    monsoon season,


    parameters such as

    humidity, temperature,

    wind speed, wind

    direction and rainfall for

    both premonsoon and

    post monsoon seasons.

    Briggs et al., (1997)

    All roads (stored as 10m grid), Mean 18 hour traffic flow

    (vehicle/hour) for each road segment, Land cover class (20 classes, stored as 10 m grid), Mean NO2 concentration (by survey period, and modeled annual mean).

    ARC/INFO version 7.1 software.

    Concentration of air pollutant NO2. Traffic volume and

    composition, traffic speed, emission factors for all main classes of vehicle, street

    characteristics (e.g. road width, building height or type) and meteorological conditions (e.g. wind speed, wind direction, atmospheric stability, mixing height).

    Mapping traffic-related air pollution within a GIS environment.

    Wong et al.,(2004)

    Mean value of air pollutants.

    Spatial interpolation technique.

    Concentration of CO, NO2, O3, lead, PM10,

    and SO2.

    Assess the role of exposure to ambient air pollutants as risk factors only for respiratory effects in children.

    Maantay et al., (2009)

    Emission rate of PM10, PM2.5, NOX, CO and SO2.



    Emission data of PM10, PM2.5, NOX, CO and

    SO2, Asthma hospitalization data with the location of each patients home address, Population data.

    Analysis of relationship between the asthma hospitalization rate and the combined impact of all selected criteria air pollutants contributed by each stationary source.

    Sengupta et al.,(1996)

    Mean monthly level of TSP, SO2 and NOx.

    GIS package GRAM software.

    Concentration of three pollutants (NO2, SO2 and Total suspended particulate matter), Density of the population.

    Assess the exposure and health risk of the population from atmospheric pollutants.

  5. Proposed Framework for Analysis and application of GIS based Air Quality monitoring

    Geostatistical analysis is used to analyze and predict the values associated with spatial or spatiotemporal phenomena. Geostatistics can be used to estimate pollutant levels and health risk foe prediction of environmental contaminant levels and their relation to the incidence rates of disease. Exhaustive studies are expensive and time consuming so Geostatistics is used to produce predictions for the unsampled locations.

    Mapping and Examine the Air pollutant data

    Mapping and Examine the Air pollutant data

    Transformation and declustering of Air pollutant data

    Model spatial structure in the dataset Generate data points that are used to generate a value for an unsampled

    In conjunction with the data set to generate interpolated value for all unsampled locations with using simple Kriging model-

    • Variography

    • Search neighborhood

      Predict values at unsampled locations Assess uncertainty of the predictions

      Selection and implementation of model along with reported uncertainties

      No Yes

      Desirable results The results can be used in

      risk analysis and decision making

  6. Conclusion

    This study concludes that Air pollution and its adverse effects on public health have require air quality management and assessment on public health. The air pollution problem originating from the various sources can be analyzed by Geostatistical analysis. The DSS based GIS analysis provide information on how much pollution exposed how many population affected and estimate environmental impacts from present and future developments so establish strategies that reduce pollution. GIS enable to integrate and analyze number of environmental data from different sources that model the overall impact of air pollutants on environment. The geostatistical analysis are used for air pollution modelling that allow the spatial variability of the elements and estimating pollution level and tools that are used for geostatistical analysis like variogram, Kriging that measure the spatial variance regarding the distance between two points and determine the spatial characteristics of the variables. By applying geostatistical methods that obtain population health risk status from information regarding cancer, respiratory and allergy diseases.

  7. Acknowledgment

    This work has been carried out at Department of GIS Cell, Motilal Nehru National Institute of Technology, Allahabad, India.

  8. Referrences

  1. Briggs David j., Collins Susan, Elliott Paul, Fischer Paul, Kingham Simon, Lebret Erik, Pryl Karel, Reeuwijk Hans Van, Smallbone Kirsty and Veen Andre van der. 1997.Mapping urban air pollution using GIS: A regression- based approach. Int. J. Geographical Information Science, vol. 11, No. 7, 699-718.

  2. Chelani A.B., Rao Chalapati C.V., Phadke K.M., Hasan

    M.Z. 2001. Formation of an Air Quality Index in India. Intern. J. Environ. Studies, Vol. 59(3), pp. 331-342.

  3. Crabbe Helen, Hamilton Ron, Machin Nuria. Using GIS and Dispersion Modelling Tools to Assess the Effect of the Environment on Health.2000.Transactions in GIS, 4(3): 235-244.

  4. Fisher Joshua B., Kelly Maggi, Romm Jeff. 2005. Scales of environmental Justice: Combining GIS and

    Spatial analysis for air toxics in West Oakland, California. Elsevier, Health & Place 12 (2006) 701-714.

  5. Jerrett M, Arain A, Kanaroglou P, Beckerman B, Potoglou D, Sahsuvaroglu T, Morrison J,Giovis C. 2005. A review and evaluation of intraurban air pollution exposure models. J Exposure Analysis Environ Epidemiol. 15:185 204.

  6. Jerrett Michael, Burnett Richard T., Ma Renjun C. Pope III Arden , Krewski Daniel, Newbold K. Bruce, Thurston George, Shi Yuanli, Finkelstein Norm, Calle Eugenia E.and Thun Michael J. Spatial Analysis of Air Pollution and Mortality in Los Angeles.2005.Epidemiology, ISSN: 1044- 3983/05/1606-0727.

  7. Joshi P. C, Semwal Mahadev.2010. Distribution of air pollutants in ambient air of district Haridwar (Uttarakhand), India: A case study after establishment of State Industrial Development Corporation. International Journal of Environmental Sciences. Volume 2, No 1.ISSN 0976-4402.

  8. Maantay Juliana A., Tu Jun, Maroko Andrew R.2008.International Journal of Environmental Health Research. Vol. 19, No.1, 59-79.

  9. Marquez Leorey O., Smith Nariida C. A framework for linking urban form and air quality.1999. Elsevier Ltd. Environmental Modelling & Software 14. 541-548.

  10. Matejicek L. Spatial modeling of air pollution in urban areas with GIS: a case study on integrated database development. Advances in Geosciences, 4, 63-68,

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