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

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.

Abstract

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	Remark
   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.
   Maantay et.al.,	GIS, Air dispersion	Developed new	Provides a relatively simple and	The air dispersion modelling
   (2009)	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
   	coupling.	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	research.
   		simulate air dispersion	expertise.
   		from stationary
   		sources for five
   		pollutants: PM10,
   		PM2.5, NOX, CO and
   		SO2.
   Fischer et.al.,	GIS and Spatial	Ripleys K method	Using Ripleys K with GIS that	Examine the spatial point
   (2006)	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		network.
   		identify the number of
   		people potentially
   		affected by air toxic.
   Matejicek	GIS, ERDAS	The spatial models and	Wide range of data collected by	Manage all the data together
   L.,(2005)	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.

   Lim et.al.,	Integrated decision	Through the	Identify high concentrations of	Identification of high
   (2005)	support system, GIS.	development of	pollutants in places such as	concentration of pollutants
   		prototype software	residential/commercial areas.	in residential/commercial
   		IMPAQT (Integrated		areas.
   		Modular Program for	Predict travel impacts on present or
   		Air Quality Tools)	future transportation systems.
   		with using a	Evaluation of existing travel
   		countywide	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
   		scenarios.	transportation models.
   Marquez et.al.,	GIS, Airshed model.	Develop a framework	The framework identifies the	This paper evaluate the
   (1999)		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,
   		quality.		the land use-transport-
   				environment module and the
   				airshed model
   Sengupta et.al.,	GIS, GRAM	Integrated existing	Gives a summary of the basic road	This integrated emission
   (1997)	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
   		component.	technology.	possibilities.
   Briggs et.al.,	GIS, Regression	With GIS regression	By using GIS that containing data	This provide pollution map
   (1997)	based approach.	based approach is used	on monitored air pollution levels,	by estimation of NO2
   		for mapping traffic	road network, traffic volume, land	concentrations.
   		related air pollutant	cover, altitude assessed predicted
   		NO2.	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			Equation	Applicability
   Briggs (1997)		et.al.,	Mean NO2 = 11.83 + (0.00398 Tvol300) + (0.268Land300) – (0.0355RSAlt) + (6.777Sampht) 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)	et	al.,	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)	et	al.,	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 et.al., (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) x t1 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 et.al., (2010)	The Oak Ridge air quality index is given by, ORAQI = 5.7 × s Ii1.37 i1 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 et.al, (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	Software	Data	Outcomes
   Jensen et.al, (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.
   Chattopadhyay	et	al,	Average concentration of	Resourcesat-1 satellite	Concentration of Air	Seasonal variation of
   (2010)			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.	approach.
   			monsoon season,
   			Meteorological
   			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.	ArcGIS 9.2, ArcV2CAD, ISC-AERMOD, SPSS software.	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.

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