Air pollutants and their ill effects on the environment and health of populations are well known. However for informed decisions on the protection of the health of populations from elevated levels of air pollution, an understanding of spatial-temporal variance of air pollutant patterns is necessary. Bangalore and other similar developing cities do not have an adequate number of fixed monitoring stations that could provide a complete coverage of the air pollution levels for the entire city. This can be overcome by using geospatial interpolation techniques that provide a complete coverage of the levels of pollutants. The aim of this study is to locate sample points, characterise distribution patterns, map air pollutant distributions using interpolation techniques, highlight areas exceeding standard levels and in doing so determine spatial and temporal patterns of the levels of air pollutants. An air pollution map indicating levels of the variability of the pollutants will aid in the analysis of effects on health in populations due to elevated levels of pollutants

Chinnaswamy, AK

Galvez, MC

Marshall, IM

Naguib, RNG

Nguyen, TQ

Olayanju, LO

Santos, GN

Shaker, MH

Ton, TN

Trodd, N

Vallar, EA

Yaacob, N

Name not available

Spatial Interpolation of Air Pollutants in Bangalore: 2010-2013

Hope's Institutional Research Archive

               7th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control,  Environment and Management  joint with 6th International Conference on Computational Intelligence and Intelligent Informatics co-located with 10th ERDT Conference  November 12-16, 2014 Hotel Centro, Puerto Prinsesa, Palawan, Philippines   Technically supported by  IEEE Philippines Section and IEEE CIS Philippines Chapter  Organized by Neuronemech   IEEE HNICEM ISCIII 2014 Co-located with ERDT Conference      Published by HNICEM   ISSN: 1908-6180               Philippines Section Spatial Interpolation of Air Pollutants in Bangalore: 2010-2013 A.K. Chinnaswamy1, R.N.G. Naguib2, Q.T. Nguyen2, L.O. Olayanju1, N. Trodd3, I.M. Marshall1, N. Yaacob1, G.N. Santos4, E.A. Vallar4, M.C. Galvez4, M.H. Shaker5 and T.N. Ton6  1Faculty of Engineering & Computing, Coventry University, UK 2BIOCORE Research & Consultancy International, UK 3Faculty of Business, Environment and Society, Coventry University, UK 4Physics Department, De La Salle University, The Philippines 5Ecology & Environment, Inc., USA 6Environmental Health Unit, World Health Organization Representative Office, Vietnam  Abstract: Air pollutants and their ill effects on the environment and health of populations are well known. However for informed decisions on the protection of the health of populations from elevated levels of air pollution, an understanding of spatial-temporal variance of air pollutant patterns is necessary. Bangalore and other similar developing cities do not have an adequate number of fixed monitoring stations that could provide a complete coverage of the air pollution levels for the entire city. This can be overcome by using geospatial interpolation techniques that provide a complete coverage of the levels of pollutants. The aim of this study is to locate sample points, characterise distribution patterns, map air pollutant distributions using interpolation techniques, highlight areas exceeding standard levels and in doing so determine spatial and temporal patterns of the levels of air pollutants. An air pollution map indicating levels of the variability of the pollutants will aid in the analysis of effects on health in populations due to elevated levels of pollutants.  INTRODUCTION Air Pollution is a growing problem in the world today and the WHO ranks air pollution as the 13th leading cause of worldwide mortality [1]. It is believed that pollution due to air quality is more harmful as opposed to pollution caused by either water or land. Air pollution may have mild effects on health such as itchy eyes, sore throat, severe effects such as breathing problems, lung cancer, etc., and inhaling severely polluted air can also result in death.  Currently developing countries face the major brunt of air pollution problems in the world. Asia reportedly is the worst affected with a large number of Asian cities having critical levels of pollution. Asia also houses the world’s worst polluted cities and it is estimated that 65% of deaths occurring in all of Asia are due to air pollution [2]. India is a fast growing economy but is burdened by increasing levels of air pollution that pose a serious threat to the environment and the wellbeing of its citizens. In a study conducted in [3], the authors estimated that exposure to ambient air pollution resulted in 627,000 premature deaths and nearly 17.8 million DALYs in India. Air quality monitoring (AQM) is essential to assess levels of air quality and impact on health. Its goal is to protect human health, the environment and the overall welfare of animal and plant life [4]. A map of air pollution levels for an entire region can assist in the management of air quality and its subsequent effects.  In most developing cities AQM stations are not available to cover the entire area or region, mainly due to the costs involved in setting up of AQMs. But to understand the effects of air quality on health, it is vital to quantify the levels at all locations. The limitations of insufficient monitoring can be overcome by the use of spatial interpolation techniques. Spatial interpolation is a procedure to predict the air concentrations of unmonitored regions based on values at measured locations within the proximity of the monitored areas [5]. Tools such as Geographic Information Systems (GIS) have gained popularity in air quality mapping and analysis. ArcGIS Geospatial Analyst facilitates the creation of a statistically valid prediction surface.  The aim of this paper is to conduct a study in Bangalore, locate monitoring stations in the city, estimate the coverage of these stations by using buffers, use spatial interpolation to determine distribution patterns, obtain statistically predicted values at unmonitored locations and determine hotspots.  METHODS Study Area Bangalore is located at 12.97◦N, 77.57◦E covering an area of 741km2 or 286 square miles. Bangalore is the third largest city in India and one of the fastest growing cities in the country, with a population of approximately 8.4 million. It is the nation’s leading information technology exporter and is popularly known as the ‘Silicon Valley of India’ [6]. Bangalore is at an average elevation of 900m (2953 feet) and, as such, the city usually enjoys a more moderate climate throughout the year compared to other cities in India. The temperatures range from an average of 15◦C in winter months to a high of 36◦C in the summer months.  Air pollution is a growing problem in the city. According to a report by TERI [7], air pollution is said to be high, severe or critical in most areas of the city. The main sources of pollution in the city are the exponential growth in the number of vehicles contributing to almost 50% of the pollution, construction activities, paved and unpaved road dust, domestic pollution and the increased use of diesel generator sets.  Air quality in the city of Bangalore is monitored through the National Ambient Air Quality Monitoring Programme and recorded by the Karnataka State Pollution Control Board (KSPCB) [8] at 6 fixed locations:  • Graphite India Limited, Whitefield Road • KHB  Industrial Area, Yelahanka • Peenya Industrial area, Regional Office Peenya • Victoria Hospital, Chamrajpet • Amco batteries, Mysore Road  • Yeshwanthpur Police Station  In addition, there are many mobile locations distributed across the city [8]. Air pollutants in the city were measured over the study period by the KSPCB in the above locations using Respirable Dust Samplers (RDS) by conventional methods. The sites are representative of the various characteristics of the city, i.e., Industrial, Commercial, Residential and Sensitive areas. Four air pollutants, namely SO2, NOx, Suspended Particulate Matter (SPM) and Respirable Suspended Particulate Matter (RSPM/PM10), are regularly monitored at all locations. The monitoring of pollutants is carried out for 24 hours (4-hourly sampling for gaseous pollutants and 8-hourly sampling for PM) with a frequency of twice a week. The KSPCB laboratories are fully equipped with experienced analysts, sophisticated equipment and approved standard test methods to carry out comprehensive analyses of the ambient air [8]. In India, the Central Pollution Control Board (CPCB) coordinates the air quality monitoring network and is responsible for pollution control [9].  CPCB sets the standard that is followed by the State Pollution Control Boards. The locations of the six monitoring stations are shown on the map in Fig. 1. Most studies obtain recorded pollution levels from the monitoring stations and average it for the whole city. But the nature of particulate matter is such that it is spatially variable, based on various factors such as size of particle, wind, location of structures and other meteorological conditions. One study suggests that the effects of particulate matter should be examined in a ½ mile radius buffer [10]. Averaging the particulate matter values over the whole city based on just 6 monitors will not provide a representative picture of the local pollution conditions.  Buffering Buffering can be described as the creation of a zone of a specified width around a point, a line or a polygon. It is also commonly referred to as the ‘zone of specified distance’ around a coverage feature. These zones or buffers are often used in queries to determine which entities occur either within or outside the defined buffer zone [11]. Buffering point data is the simplest form of buffering that involves the creation of a circular polygon around each point of radius equal to the buffer distance. Applying the ½ mile (800m) radius buffer, the coverage of the monitoring stations for Bangalore was generated (Fig. 2).  It is evident from the map that the stations do not provide coverage of the entire city. To obtain the values at unrecorded locations in the city, geostatistical interpolation methods are employed and a continuous surface for the entire city is derived.      Fig. 1: AQMs in Ba   Data The data compiled for thesepollutants NOx, SO2 and PM10 wKSPCB for the years 2010-2013into the database of the respective stations. The vcorresponding years and stationthe levels are shown in the graphThe standards for the levelcomparison with WHO standastandards are provided in Table 1 Spatial Interpolation Methods After inputting all the vadistributions of these pollutantsusing Inverse Distance Weighted                   Fig. 3: SO2 levels 20ngalore                  Fig. 2: Buf 6 stations for the ere obtained from . That was entered GIS for the  alues for the s and the trend of  Figs. 3-5.   of pollutants in rds and US-EPA . lues, the spatial  were determined  (IDW)      interpolation technitechniques based primand Kriging. IDW is cdistance between obssite at which the predAs the number of monKriging is problematlimited number of ssmooth the spatial panot capturing the pollutant [12]. IDW scenario due to its capto nearby points 10-2013                             Fig. 4: NOx fer of AQMS ques. The interpolation arily on distance are IDW omputed as a function of the erved sample sites and the iction has to be made [12]. itors in Bangalore is limited, ic to use since, for are a patial observations, it can ttern of pollution levels by spatial complexity of the is thus more useful in this ability to assign more weight than to distant points. levels 2010-2013     Fig. 5: PM10 levels 2010-2013 RESULTS AND MAPS  Figs. 6-7 represent the inteSO2 levels for the years 2010-20and the maps it can be discerneare below the recommendeBangalore. In 2010, the levels13.3µg/m3 and 16.5µg/m3, recommended standard of 50µglevels recorded among the four ythe highest level being 19.3µgbelow the recommended standlevels were similar to levels recolevels ranging between 13µg/m3 lightest green shade in the maps levels of SO2 whereas the dardenotes the highest level. Figs. 10-13 represent the intNOx levels for the years 2010-20and maps it can be discerned thbelow the recommended standaIn 2010, the levels ranged betwe37.9µg/m3. In 2011, the highest r30.4µg/m3. In 2013, the levels inbetween 30-31µg/m3. Again, tshade in the maps depicts the lowand the darkest green shade highest level. Figs. 14-17 represent the mafor the years 2010-2013. PM10 havariability in the city. In 2010, thlevel was 56µg/m3 and the highalmost 3.7 times the recomme60µg/m3 and 11 times the recTable 1: Standards for rpolated maps of 13. From Table 1 d that SO2 levels d standards in  ranged between well below the /m3. The highest ears was in 2012, /m3 but still well ard. In 2013, the rded in 2010 with and 16µg/m3. The depicts the lowest kest green shade erpolated maps of 13. From the table at NOx levels are rds in Bangalore. en 33.4µg/m3 and ecorded level was  all the areas were he lightest green est levels of NOx denotes has the ps of PM10 levels s a wider range of e lowest recorded est was 221µg/m3, nded standard of ommended WHO standard of 20µg/m3. the highest levels of PThe areas belowdetermined and only 5below the recommrepresents the arearecommended standaabove the recommendareas that exceed twicIn 2011 all 198 wrecommended standabetween 64µg/m3 and In 2012 levels fell the city. Seventeen (1below the recommeshowing levels belowand areas with twice 20 and 21. The yearlevels of PM across thmore than twice the the highest level beinstandard. While thcharacterise the distrcity, care has to be tprocess with these necessary to understapredictions. PM10 has nature. In [10] it wahighways with heavvariability of the polAnother study suggecitywide air quality aand thus individual gbe very significant becomes a problemeconomically feasibleblock. Industrial, residential and ruralareas (µg/m3)Sa(µANNUAL 4024 HOURS 80ANNUAL 5024 HOURS 80ANNUAL 6024 HOURS 100PollutantTime weighted averageINDIANOxSO2PM10NOx, SO2 and PM10 The darkest red areas have M10 pollution.   60µg/m3 in 2010 are  wards out of 198 had levels ended standards. Fig. 18 s with levels below the rd; 193 wards had levels ed level. Fig. 19 represents e the recommended standard. ards had levels above the rd, with levels recorded 122µg/m3 across the city. slightly across some areas in 7) wards had predicted levels nded standard; the maps  the recommended standard the levels are shown in Figs.  2013 recorded the highest e city. All stations recorded recommended standard with g 3 times the recommended e maps in Figs. 14-17 ibution of PM10 across the aken in the decision making predicted values as it is nd the uncertainty of these a spatial variability due to its s suggested that in case of y vehicle movement, the lutants are limited to 150m. sted that studies based on dvocate that local exposure, radients in air quality, could [13]. The question then  of scale as it is not  to have an AQM on every WHO US-EPAensitive reas g/m3)(µg/m3) (µg/m3)30 40 10080 - -20 - 8080 20 36560 20 60100 50 150        Fig 6: Interpolated SO2 – 2010              Fig 7: Interpolated SO2 – 2011          Fig. 8: Interpolated SO2 – 2012        Fig. 9: Interpolated SO2 – 2013               Fig. 10: Interpolated NOx – 2010                        Fig. 11: Interpolated NOx – 2011            Fig. 12: Interpolated NOx – 2012                      Fig. 13: Interpolated NOx – 2013             Fig. 14: Interpolated PM10 – 2010                   Fig. 15: Interpolated PM10 – 2011             Fig. 16: Interpolated PM10 – 2012                Fig. 17: Interpolated PM10 – 2013     Fig. 10: Wards with PM1o levels < 60µg/m3                   Fig. 11:Wards with PM1o levels > 120µg/m3 Fig. 20: Wards with PM1o levels < 60µg/m3                      Fig. 21: Wards with PM1o levels > 120µg/m3  CONCLUSIONS  This paper has utilised the capabilities of GIS to locate the monitoring stations, buffer the zones to show the extent of the pollutant, use interpolation techniques to characterise distributions, perform queries to answer questions and obtain values at every location in the city that will assist in further analyses to determine levels of pollutants and their effects on health of the citizens. The paper has demonstrated that IDW can be a powerful tool to determine concentrations of pollutants and that the strong capabilities of GIS can be utilised to determine locations affected by high levels of pollution.  From the maps, it is observed that there is a serious problem with the levels of PM10 in the city. PM causes decreased lung function, development of chronic bronchitis and premature death. Short-term exposures are associated with increased respiratory symptoms, cardiac arrhythmias, heart attacks, hospital admissions, emergency room visits for heart or lung disease and premature death [14].  The all-cause daily mortality is estimated to increase by 0.2-0.6% per 10µg/m3 of PM10 [15]. There are no studies or evidence to indicate safe levels of PM or a threshold below which there are no adverse effects on health. However studies have demonstrated that average life expectancy has increased by reducing levels of air pollution through interventions and reductions in pollution.  In Utah Valley, USA, the closure of a steel mill led to a decrease of 50% of PM10 levels that resulted in a reduction of hospital admissions by 3 times and admissions for asthma and bronchitis illnesses halved. Furthermore a 15µg/m3 decrease in levels of PM10 led to a 3.2% drop in the daily number of deaths [16, 17]. Since the burden of air pollution on health is significant, even at relatively low concentrations, the effective management of air quality is necessary to reduce the health risks to a minimum. Bangalore and other cities in India can benefit from knowledge, communication and decision making on air pollution and health.  REFERENCES [1] WHO (2002) The World Health Report 2002 - Reducing Risks, Promoting Healthy Life, World Health Organisation, Geneva, Switzerland. [2] Conserve Energy Facts (2014) Air Pollution Facts, [online] available from: <http://www.conserve-energy-future.com/various-air-pollution-facts.php>[July 15, 2014]. [3] Balakrishnan, K., Cohen, A., Smith, K., R. (2014)  ‘Addressing the Burden of Disease Attributable to Air Pollution in India: The Need to Integrate across Household and Ambient Air Pollution Exposures.’ Environmental Health Perspectives. 122: 1 [4] Haq, G. and Schwela, D. (2008) Urban Air Pollution in Asia, Stockholm Environment Institute, Stockholm, Sweden. [5] US EPA (2004) Developing Spatially Interpolated Surfaces and Estimating Uncertainty, [online] available from <http://www.epa.gov/airtrends/specialstudies/dsisurfaces.pdf> [July 10, 2014]. [6] Sudhira, H.S., Ramachandra, T.V., and Bala Subrahmanya, M.H. (2007) 'City Profile Bangalore', Cities, 24, (5): 379–390.  [7] CPCB (2010) Air Quality Assessment, Emission Inventory and Source Apportionment Study for Indian Cities [online] available from: <http://www.moef.nic.in/downloads/public-information/Rpt-air-monitoring-17-01-2011.pdf> [June 14, 2011] [8] KSPCB (2012) Ambient Air Quality Monitoring, [online] available from:   <http://kspcb.gov.in/ambient_air_quality.htm> [June11, 2011].  [9] CPCB (2006) Air Quality Trends and Action Plan for Control of Air Pollution from  Seventeen Cities, Ministry of Environment & Forests, India.  [10] Mantay, J. (2007) Asthma and air pollution in the Bronx: Methodological and data considerations in using GIS for environmental justice and health research. Health and Place. 13: 32-56 [11] ESRI (2013) Buffer Analysis [online] available from <http://resources.arcgis.com/en/help/main/10.1/index.html - //000800000019000000> [January 10, 2014]  [12] Tecer, L.,H., Tagil, S. (2013) Spatial-Temporal Variations of Sulphur Dioxide Concentration, Source, and Probability Assessment Using a GIS-Based Geostatistical Approach. Polish Journal of Environmental Studies 22, (5): 1491-1498 [13] Lipfert, F. (2004) Air pollution and poverty: Does the sword cut both ways? Journal of Epidemiology and Community Health. 58:2-3 [14] WHO (2008) Air Quality and Health, World Health Organisation, Geneva, Switzerland.  [15] Samoli E et al. Acute effects of ambient particulate matter on mortality in Europe and North America: results from the APHENA Study. Environmental Health Perspectives, 2008, 116(11):1480–1486. [16] Pope CA III. Respiratory disease associated with community air pollution and a steel mill, Utah Valley. American Journal of Public Health, 1989, 79(5):623–628. [17] Pope CA III. Daily mortality and PM10 pollution in Utah Valley. Archives of Environmental  Health, 1992, 47(3):211-217. 

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Spatial Interpolation of Air Pollutants in Bangalore: 2010-2013

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